Phase 3: AI API Endpoints & Worker Integration

[brendadeeznuts1111]

🚀 Phase 3: AI API Endpoints & Worker Integration

Summary

Implements Phase 3 of the Kimi K2 AI integration, adding API endpoints and MCP tools for AI-powered betting analysis.

Changes

• ✅ AI Chat API Endpoint (POST /api/ai/chat)

  • Request validation and error handling
  • Platform context enrichment from D1
  • Cost tracking and usage reporting

• ✅ 3 AI-Enhanced MCP Tools

  • aiSharpAnalysis: AI-powered customer profiling
  • aiSteamDetection: AI-powered line movement detection
  • aiRiskReport: AI-powered risk assessment

• ✅ Worker Integration

  • Updated routing in src/index.ts
  • Registered tools in MCP system
  • Added tool definitions

• ✅ Documentation

  • Added branching strategy guide
  • Created Phase 3 completion report

Files Changed

• New: 5 files (+684 lines)
• Modified: 3 files

Testing

• Manual testing of AI chat endpoint
• Manual testing of MCP tools
• Integration testing with D1 database

Related Issues

Part of Kimi K2 AI Integration (Phases 1-6)

Checklist

• Code follows project style guidelines
• Changes are documented
• Commit messages follow conventions
• Tests pass (unit tests to be added in Phase 5)
• No breaking changes

Next Steps

After merge:

• Phase 4: Data pipeline & real-time integration
• Phase 5: Testing & validation
• Phase 6: Production deployment

See PHASE_3_COMPLETE.md for detailed documentation.

Summary by CodeRabbit

• New Features

  • Added AI Chat API with context enrichment, usage/cost reporting, and CORS.
  • Introduced AI Query endpoint for natural-language intents (sharp analysis, steam detection, risk report, agent performance, general chat).
  • Added AI Health endpoint summarizing service and database connectivity.
  • Integrated AI-powered tools: Sharp Analysis, Steam Detection, and Risk Report.

• Documentation

  • Added branching strategy guide.
  • Added Phase 3 completion notes detailing new AI endpoints and tools.

• Chores

  • Added project-wide formatting and linting configs and pre-commit checks.

[coderabbitai]

Walkthrough

Adds AI endpoints (/api/ai/health, /api/ai/query, /api/ai/chat), three MCP AI tool handlers (sharp analysis, steam detection, risk report), registry/tool definitions, and configuration files (.editorconfig, ESLint, Prettier, Husky). Includes documentation for branching strategy and a Phase 3 summary. No changes to existing non-AI routes.

Changes

Cohort / File(s)	Summary
Documentation BRANCHING_STRATEGY.md, PHASE_3_COMPLETE.md	New docs: branching strategy and Phase 3 summary describing AI endpoints, MCP tools, and integration points.
API Endpoints src/api/ai-chat.ts, src/api/ai-health.ts, src/api/ai-query.ts	New handlers: AI chat (validation, optional context, BettingAnalyzer call, CORS), AI health (Kimi and DB checks, statuses), AI query (NL parsing to intents, dispatch to executors, structured responses).
Routing src/index.ts	Wires new routes: GET /api/ai/health, POST /api/ai/query, POST /api/ai/chat; delegates to new handlers.
MCP Handlers src/mcp/handlers/aiSharpAnalysis.ts, src/mcp/handlers/aiSteamDetection.ts, src/mcp/handlers/aiRiskReport.ts	Adds three MCP tool handlers: input validation, DB queries, BettingAnalyzer usage, structured MCPToolResult with usage/cost and timestamps; error handling.
MCP Registry src/mcp/toolRegistry.ts	Imports new handlers; registers AI tools via registerAITools; invoked during initialization.
MCP Tools Definition src/mcp/tools.ts	Adds AI tool entries and integrates into getMCPTools. Note: duplicate getAITools definitions with slightly differing schemas.
Tooling / Config .editorconfig, .eslintrc.json, .prettierrc.json, .husky/pre-commit	Adds formatting and linting configs; Husky pre-commit adds formatting check, adjusts security check behavior, placeholders for tests/lint.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  participant C as Client
  participant W as Worker (index.ts)
  participant H as handleAIChat
  participant A as Analytics DB
  participant K as Kimi API (BettingAnalyzer)

  C->>W: POST /api/ai/chat {messages, includeContext, maxTokens}
  W->>H: handleAIChat(request, env)
  H->>H: Validate body / defaults
  alt includeContext == true
    H->>A: Fetch platform context
    A-->>H: Context data or none
    H->>H: Insert system context message (if available)
  end
  H->>K: Analyze chat (messages, maxTokens, apiKey)
  K-->>H: {text, usage, cost}
  H-->>W: JSON {text, usage, cost, context?, ts}
  W-->>C: 200 with CORS
  opt Error paths
    H-->>W: JSON error {code, message, ts}
    W-->>C: 4xx/5xx with CORS
  end

Loading

sequenceDiagram
  autonumber
  participant C as Client
  participant W as Worker (index.ts)
  participant Q as handleAIQuery
  participant DB as D1 (Analytics/Raw)
  participant K as Kimi API

  C->>W: POST /api/ai/query {query, agentID?}
  W->>Q: handleAIQuery(request, env)
  Q->>Q: Validate API key, parse intent
  alt sharp_analysis / steam_detection / risk_report
    Q->>DB: Fetch required data
    DB-->>Q: Data
    Q->>K: Run analysis (BettingAnalyzer)
    K-->>Q: Result + usage/cost
  else agent_performance
    Q->>DB: Query top agents
    DB-->>Q: Aggregates
  else general_chat
    Q->>K: Chat response
    K-->>Q: Text + usage/cost
  end
  Q-->>W: JSON {query, intent, result, usage?, cost?, ts}
  W-->>C: 200 or error code

Loading

sequenceDiagram
  autonumber
  participant C as Client
  participant W as Worker (index.ts)
  participant H as handleHealthCheck
  participant A as ANALYTICS DB
  participant R as RAW_FEED_DB
  participant K as Kimi API

  C->>W: GET /api/ai/health
  W->>H: handleHealthCheck(request, env)
  H->>K: Probe connectivity (apiKey)
  par DB checks
    H->>A: SELECT count(tables)
    H->>R: SELECT count(tables)
  end
  A-->>H: Status
  R-->>H: Status
  K-->>H: Status
  H-->>W: JSON {status, kimi, databases[], ts, version}
  W-->>C: 200/206/503 with CORS

Loading

Estimated code review effort

🎯 4 (Complex) | ⏱️ ~60 minutes

Possibly related PRs

• 🚀 MCP Integration & Complete System Documentation #1 — Also adds MCP tool handlers and updates src/mcp/handlers, src/mcp/toolRegistry.ts, and src/mcp/tools.ts, overlapping with this PR’s MCP integration.

Poem

In burrows of code I nibble and chat,
New routes hop in—health, query, and chat!
Three clever tools twitch whiskers in sync,
Steam, sharp, and risk with a caffeinated blink.
With tidy lint and pretty attire,
My paws approve—commit, don’t tire.
Thump! Phase 3’s on fire. 🐇✨

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description Check	⚠️ Warning	The pull request description does not follow the repository’s required template, as it lacks a “Fixes #” issue reference, a standardized “Type of change” section, and the full checklist sections for AI Development Standards, security, and other required headings specified in the template.	Please update the description to include the “Fixes #” line specifying the issue, add the “Type of change” section, and fill out all required checklist sections (AI Development Standards, Code Quality, Testing & Validation, Security, Documentation, and Style Guide Rules Update) to match the repository’s template.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The title “Phase 3: AI API Endpoints & Worker Integration” clearly and concisely summarizes the primary purpose of the pull request by indicating it implements phase 3 with new AI API endpoints and worker integration, so it accurately reflects the main changes in the diff.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch feature/kimi-ai-phase3-api-endpoints

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[coderabbitai]

Actionable comments posted: 7

🧹 Nitpick comments (4)

src/mcp/toolRegistry.ts (1)

181-194: Add requestId propagation and structured logging/errors in registry.

Per guidelines for src/mcp/**: generate a requestId, include it in logs and JSON responses, and log duration. Recommend passing requestId into handlers and including it in error payloads.

As per coding guidelines

@@
 function registerAITools() {
   // AI Sharp Analysis
   toolRegistry.set('aiSharpAnalysis', getAISharpAnalysis);
@@
   // AI Risk Report
   toolRegistry.set('aiRiskReport', getAIRiskReport);
 }
 
@@
 export async function callTool(
   toolName: string,
   args: Record<string, any>,
   env: MCPEnv
 ): Promise<MCPToolResult> {
   // Initialize registry on first call
   initializeRegistry();
 
+  const requestId = Date.now().toString(36);
+  const startedAt = Date.now();
+
   const handler = toolRegistry.get(toolName);
 
   if (!handler) {
     return {
       content: [
         {
           type: 'text',
-          text: `Tool not found: ${toolName}\n\nAvailable tools: ${Array.from(toolRegistry.keys()).join(', ')}`,
+          text: JSON.stringify({
+            error: 'Tool not found',
+            tool: toolName,
+            requestId,
+            availableTools: Array.from(toolRegistry.keys()),
+          }, null, 2),
         },
       ],
       isError: true,
     };
   }
 
   try {
-    console.log(`[MCP] Executing tool: ${toolName}`, { args });
-    const result = await handler(args, env);
-    console.log(`[MCP] Tool completed: ${toolName}`, {
-      isError: result.isError || false,
-    });
+    console.log(`[${requestId}] [MCP] Executing tool: ${toolName}`, { args });
+    const result = await handler({ ...args, requestId }, env);
+    const durationMs = Date.now() - startedAt;
+    console.log(`[${requestId}] [MCP] Tool completed: ${toolName}`, {
+      isError: result.isError || false,
+      durationMs,
+    });
     return result;
   } catch (error) {
-    console.error(`[MCP] Tool execution failed: ${toolName}`, error);
-    return createErrorResult(error);
+    const durationMs = Date.now() - startedAt;
+    console.error(`[${requestId}] [MCP] Tool execution failed: ${toolName}`, { error, durationMs });
+    return createErrorResult(error, requestId);
   }
 }
 
@@
-function createErrorResult(error: unknown): MCPToolResult {
+function createErrorResult(error: unknown, requestId?: string): MCPToolResult {
   const message = error instanceof Error ? error.message : String(error);
   return {
     content: [
       {
         type: 'text',
-        text: `Error: ${message}`,
+        text: JSON.stringify({ error: message, requestId }, null, 2),
       },
     ],
     isError: true,
   };
 }

Optionally thread requestId into existing internal wrappers:

@@
-    } catch (error) {
-      return createErrorResult(error);
+    } catch (error) {
+      return createErrorResult(error, (args as any)?.requestId);
     }

src/mcp/tools.ts (1)

444-489: Schemas and names align with handlers; consider ID casing consistency.

Definitions match handler names/args. Optional: standardize ID casing across tools (customerId vs customerID, eventId vs eventID) to reduce client confusion.

src/mcp/handlers/aiSharpAnalysis.ts (2)

27-34: Cast D1 result via as unknown as per project convention.

Aligns with D1 typing guideline.

As per coding guidelines

-    const customer = await env.ANALYTICS.prepare(`
+    const customer = (await env.ANALYTICS.prepare(`
       SELECT cid, clv, wr, ao, nb, upd
       FROM sharp_indicators
       WHERE cid = ?
       LIMIT 1
-    `).bind(customerId).first<CustomerData>();
+    `)
+      .bind(customerId)
+      .first()) as unknown as CustomerData;

---

49-66: Include requestId and duration in success path.

Return requestId and log completion with duration.

As per coding guidelines

       aiUsage: { inputTokens: result.usage.inputTokens, outputTokens: result.usage.outputTokens, totalTokens: result.usage.totalTokens, cost: result.cost.totalCost },
-      timestamp: new Date().toISOString(),
+      requestId,
+      timestamp: new Date().toISOString(),
     };
 
-    return { content: [{ type: 'text', text: JSON.stringify(response, null, 2) }], isError: false };
+    const durationMs = Date.now() - startedAt;
+    console.log(`[${requestId}] aiSharpAnalysis completed`, { durationMs, isError: false });
+    return { content: [{ type: 'text', text: JSON.stringify(response, null, 2) }], isError: false };

📜 Review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 2f0275f and 558fa44.

📒 Files selected for processing (8)

• BRANCHING_STRATEGY.md (1 hunks)
• src/api/ai-chat.ts (1 hunks)
• src/index.ts (2 hunks)
• src/mcp/handlers/aiRiskReport.ts (1 hunks)
• src/mcp/handlers/aiSharpAnalysis.ts (1 hunks)
• src/mcp/handlers/aiSteamDetection.ts (1 hunks)
• src/mcp/toolRegistry.ts (3 hunks)
• src/mcp/tools.ts (2 hunks)

🧰 Additional context used

📓 Path-based instructions (22)

src/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/cloudflare-workers.mdc)

Use the Env, BetTickerSnifferEnv, and MCPEnv types from src/types/api.ts across TypeScript sources

Use @cloudflare/workers-types for Cloudflare Worker TypeScript definitions across source files

src/**/*.ts: Always use parameterized queries with .bind() for all D1 queries
Never use string interpolation in SQL for D1 queries
Always cast D1 results with as unknown as <Type> when consuming D1Result
Always limit query results (use LIMIT) in D1 queries
Always handle database errors around D1 operations (try/catch and propagate meaningful errors)
Never forget to bind parameters when executing D1 queries
Never ignore database errors from D1 operations

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

src/{index.ts,mcp/**/*.ts,tools/**/*.ts,interceptors/**/*.ts}

📄 CodeRabbit inference engine (.cursor/rules/endpoint-routing.mdc)

src/{index.ts,mcp/**/*.ts,tools/**/*.ts,interceptors/**/*.ts}: Always generate and use a requestId; include it in logs and in JSON responses
Always include CORS headers (Access-Control-Allow-*) on responses and handle OPTIONS preflight
Use standardized error handling: try/catch with JSON error payload { error, message, requestId } and proper headers including CORS
Log request duration with requestId (startTime, duration in ms) upon completion

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

src/mcp/handlers/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/endpoint-routing.mdc)

Create new MCP handler modules under src/mcp/handlers/ matching the tool name and exported signature

src/mcp/handlers/**/*.ts: Place each new MCP tool handler in src/mcp/handlers as its own module exporting an async function that returns MCPToolResult and uses MCPEnv
Always return isError: true in MCPToolResult for failure cases within handlers
Validate all args before executing any database queries in handlers
Use a dedicated TypeScript interface/type for handler args and type the args parameter accordingly; import MCPToolResult and MCPEnv from ../types
Optimize database queries in handlers to target under 500ms execution time

src/mcp/handlers/**/*.ts: Create MCP tool handlers under src/mcp/handlers/ and validate args before DB queries
MCP handlers must return MCPToolResult format

Place all MCP tool handlers under src/mcp/handlers as individual TypeScript files (e.g., steamMoves.ts, riskConcentration.ts)

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/handlers/aiSharpAnalysis.ts

**/*.{ts,tsx,js,jsx}

📄 CodeRabbit inference engine (.cursorrules)

**/*.{ts,tsx,js,jsx}: Use Bun runtime and tooling: bun commands only; use Bun file APIs; leverage Bun native APIs; and use processManager for spawning
Never use parseFloat() for stakes; use Number() instead
Use explicit UTC timestamps instead of new Date() defaults

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

**/*.{ts,tsx}

📄 CodeRabbit inference engine (.cursorrules)

**/*.{ts,tsx}: Use standardized error classes by importing from src/utils/error-handler.ts
Always use try-catch or an asyncHandler wrapper around async request handlers
Return structured error responses and avoid exposing internal details
Always cast D1 results with as unknown as when necessary
Always handle database errors
Always validate all inputs using Zod schemas before processing
Always free WASM memory in finally blocks
Always generate a requestId (Date.now().toString(36)) and prefix logs with it
Always include CORS headers and handle OPTIONS preflight for HTTP endpoints
Always use processManager.spawn() with timeouts and cleanup in finally; never leave zombie processes

**/*.{ts,tsx}: Never use string interpolation in SQL queries; always use parameterized queries with placeholders and .bind(...)
All API responses must include CORS headers (e.g., Access-Control-Allow-Origin) when returning Response objects
All console logs should be structured to include the requestId prefix

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

**/*.{ts,tsx,sql}

📄 CodeRabbit inference engine (.cursorrules)

Database queries must be parameterized (use .bind()), never string interpolation

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

**/*.{ts,tsx,jsx,js}

📄 CodeRabbit inference engine (.cursorrules)

Always sanitize output to prevent XSS; never trust user input

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

{src/index.ts,src/interceptors/**/*.ts,src/tools/intelligence/**/*.ts,src/mcp/**/*.ts}

📄 CodeRabbit inference engine (CLAUDE.md)

Validate all external inputs and outputs with Zod (API params, BetTicker metadata, tool I/O, MCP messages)

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

**/*.{ts,tsx,js,mjs,cjs}

📄 CodeRabbit inference engine (.cursor/rules/bun-runtime.mdc)

**/*.{ts,tsx,js,mjs,cjs}: Use the shared process manager (import processManager from './tests/utils/process-cleanup') for all process spawning
Do not call Bun.spawn() directly
Do not use Node.js child_process for spawning
Use Bun file APIs (Bun.file, Bun.write, etc.) instead of Node.js fs or fs/promises
Prefer Bun’s native APIs (crypto, HTTP client, streams, etc.) over external libraries when those features are needed

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

**/*

📄 CodeRabbit inference engine (.cursor/rules/code-searchability.mdc)

Avoid hardcoded URLs in code; reference configuration/constants instead

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• BRANCHING_STRATEGY.md
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

src/mcp/handlers/*.ts

📄 CodeRabbit inference engine (.cursor/rules/file-naming.mdc)

MCP handler filenames must be camelCase to match tool names

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/handlers/aiSharpAnalysis.ts

**/*.{ts,tsx,js,jsx,mjs,cjs}

📄 CodeRabbit inference engine (.cursor/rules/production-security.mdc)

**/*.{ts,tsx,js,jsx,mjs,cjs}: Do not use parseFloat() for betting stakes; use Number() and validate with isNaN() and > 0 before accepting input
For Cloudflare D1 queries, never interpolate values into SQL strings; use parameterized queries with .prepare(...).bind(...)
When sending to Cloudflare Queues, limit each batch to a maximum of 100 messages (chunk sends accordingly)
Avoid using new Date() without arguments in Edge Workers; prefer Date.now() or explicit UTC (e.g., toISOString())
Manage WASM memory explicitly: allocate, use, and free in a finally block to prevent leaks

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

!(README|CLAUDE).md

📄 CodeRabbit inference engine (.cursor/rules/root-organization.mdc)

Do not place Markdown files in the root except README.md and CLAUDE.md

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• BRANCHING_STRATEGY.md
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

{src,scripts}/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/security-patterns.mdc)

{src,scripts}/**/*.ts: Always validate all inputs before use (type, length, bounds)
Always use parameterized queries; never interpolate variables into SQL strings
Always sanitize output to prevent XSS when rendering or embedding user-controlled data
Always validate JWT or auth tokens (verify signature and expiration) before trusting them
Always implement rate limiting for request handling paths
Always hash sensitive data (e.g., passwords) using strong algorithms (e.g., bcrypt) before storage
Always use secure, generic error messages; log details internally only
Always sanitize/redact sensitive fields (password, token, apiKey) in logs
Always use restrictive CORS configuration (specific origins, methods, headers)
Always validate required environment variables before use
Never expose internal error details (messages, stacks) in HTTP responses
Never hardcode secrets; load secrets from environment or secret manager and validate presence
Never omit or ignore standard security headers in responses (e.g., HSTS, X-Frame-Options, CSP, X-Content-Type-Options)
Use authenticated encryption for sensitive data at rest/in transit (e.g., AES-GCM)
Validate uploaded files (MIME type whitelist and maximum size)
Prevent path traversal by validating and normalizing file paths; reject paths with '..', absolute roots, or excessive length
Set standard security headers on all HTTP responses (e.g., HSTS, CSP, X-Frame-Options, X-Content-Type-Options, X-XSS-Protection)

Files:

• src/mcp/handlers/aiSteamDetection.ts
• src/api/ai-chat.ts
• src/mcp/handlers/aiRiskReport.ts
• src/mcp/toolRegistry.ts
• src/index.ts
• src/mcp/handlers/aiSharpAnalysis.ts
• src/mcp/tools.ts

src/api/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/api-patterns.mdc)

src/api/**/*.ts: In endpoint code, throw typed errors via Errors.* and format failures with createErrorResponse(error, requestId, path)
Use specific Errors.* types to map to correct HTTP codes (badRequest, unauthorized, forbidden, notFound, validationError, rateLimit, databaseError, serviceUnavailable, timeout)
Prefer wrapping handlers with asyncHandler(...) so thrown errors are auto-caught and formatted
At the start of each handler, validate required environment variables with validateEnv(env, [...])
Validate incoming input using validateQueryParams/Validators and return validationErrorResponse on failure
Use parameterized D1 queries with .prepare(...).bind(...).all() and never interpolate variables into SQL strings
When reading D1 results, cast results via unknown as needed to strongly-typed arrays/objects
Always include standard CORS headers in responses: Access-Control-Allow-Origin '*', Content-Type 'application/json' (and methods/headers as needed)
Generate a requestId at the start of each request (Date.now().toString(36)), log with it, include it in success payloads and in createErrorResponse
Structure success responses as JSON with data payload plus metadata: requestId and timestamp; include pagination fields when applicable
Log appropriately per request: incoming, success summaries, and errors, all prefixed with [requestId]

All endpoint handlers must generate and use a requestId (e.g., include requestId: string in handler signature and use it in logs)

Files:

• src/api/ai-chat.ts

src/mcp/toolRegistry.ts

📄 CodeRabbit inference engine (.cursor/rules/endpoint-routing.mdc)

Register new MCP handlers in src/mcp/toolRegistry.ts (add case/registry entry)

Register each new tool in src/mcp/toolRegistry.ts by importing its handler and adding a switch case mapping the tool name to the handler call

Register each MCP tool in src/mcp/toolRegistry.ts

Map MCP tool names to handler functions in src/mcp/toolRegistry.ts (registry-based dispatch)

Files:

• src/mcp/toolRegistry.ts

src/index.ts

📄 CodeRabbit inference engine (.cursor/rules/cloudflare-workers.mdc)

src/index.ts: Export a default Worker with an async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise handler in src/index.ts
Route requests by URL pathname in the fetch handler with cases for /health, /mcp, and /tools/getBettingExposure
Always include CORS headers (Access-Control-Allow-Origin, Access-Control-Allow-Methods, Access-Control-Allow-Headers) on responses for dashboard access
Generate and log a per-request ID (e.g., Date.now().toString(36)) for request tracing
Use ctx.waitUntil(...) for non-blocking async work (e.g., KV writes) during request handling
Implement a queue consumer via export default { async queue(batch: MessageBatch, env: Env) } and acknowledge each message (message.ack()) after processing
Implement scheduled tasks via export default { async scheduled(event: ScheduledEvent, env: Env, ctx: ExecutionContext) }
Apply cost guard logic from src/guards/costCap.ts to enforce hard cost limits, graceful degradation, and request quotas in request handling

src/index.ts: All HTTP requests must be handled in src/index.ts via the top-level fetch() handler
Main request flow in fetch(): generate requestId, parse URL, log with requestId, handle OPTIONS (CORS preflight), route by pathname, and return responses with CORS headers
Core system endpoints must use exact pathname matches for /health, /logs, /diagnostics, and /system-status
Route /cloud/api/Manager/getBetTicker to the BetTicker interceptor handler for transparent proxying with KV interception
Handle BetTicker history/analysis API under /interceptor/* via handleInterceptorAPI() supporting /interceptor/{history,response,stats} routes
Route /mcp requests to the MCP JSON-RPC handler (handleMCPRequest)
Route /tools/* to handleMCPTools() for intelligence tools endpoints
For complex routes, delegate to separate handler functions (e.g., handleDiagnostics, handleLogs, handleSystemStatus, handleMCPTools, handleInterceptorAPI)
When adding a new core/system endpoint, add an exact-m...

Files:

• src/index.ts

{src/index.ts,src/queues/**/*.ts,src/schedules/**/*.ts,src/interceptors/**/*.ts,src/mcp/server.ts}

📄 CodeRabbit inference engine (CLAUDE.md)

Use ctx.waitUntil() for async side-effects to avoid delaying responses in Workers

Files:

• src/index.ts

**/*.md

📄 CodeRabbit inference engine (.cursor/rules/documentation.mdc)

All documentation Markdown files must be placed under docs/ (only README.md, LICENSE, and CLAUDE.md may live in the repository root)

Files:

• BRANCHING_STRATEGY.md

*.md

📄 CodeRabbit inference engine (.cursor/rules/documentation.mdc)

Do not create Markdown documentation files in the repository root except README.md, LICENSE, and CLAUDE.md

Files:

• BRANCHING_STRATEGY.md

*

📄 CodeRabbit inference engine (.cursor/rules/root-organization.mdc)

Only these files are permitted in the repository root: README.md, LICENSE, CLAUDE.md, package.json, package-lock.json, bun.lock, tsconfig.json, wrangler.toml, wrangler.staging.toml, wrangler.production.toml

Files:

• BRANCHING_STRATEGY.md

src/mcp/tools.ts

📄 CodeRabbit inference engine (.cursor/rules/endpoint-routing.mdc)

Register new tool definitions with schema in src/mcp/tools.ts

src/mcp/tools.ts: Define each new tool in src/mcp/tools.ts with name, clear description, and a JSON Schema inputSchema including required and optional properties
Provide clear, user-facing descriptions for each tool definition

Define MCP tools and schemas in src/mcp/tools.ts

Define MCP tool schemas, descriptions, and parameters in src/mcp/tools.ts

Files:

• src/mcp/tools.ts

🧠 Learnings (13)

📚 Learning: 2025-10-07T19:32:40.879Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/mcp-integration.mdc:0-0
Timestamp: 2025-10-07T19:32:40.879Z
Learning: Applies to src/mcp/toolRegistry.ts : Register each new tool in src/mcp/toolRegistry.ts by importing its handler and adding a switch case mapping the tool name to the handler call

Applied to files:

• src/mcp/toolRegistry.ts

📚 Learning: 2025-10-08T04:34:43.736Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursorrules:0-0
Timestamp: 2025-10-08T04:34:43.736Z
Learning: Applies to src/mcp/toolRegistry.ts : Register each MCP tool in src/mcp/toolRegistry.ts

Applied to files:

• src/mcp/toolRegistry.ts
• src/mcp/tools.ts

📚 Learning: 2025-10-07T19:31:32.730Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/endpoint-routing.mdc:0-0
Timestamp: 2025-10-07T19:31:32.730Z
Learning: Applies to src/mcp/toolRegistry.ts : Register new MCP handlers in src/mcp/toolRegistry.ts (add case/registry entry)

Applied to files:

• src/mcp/toolRegistry.ts

📚 Learning: 2025-10-08T04:36:05.383Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: CLAUDE.md:0-0
Timestamp: 2025-10-08T04:36:05.383Z
Learning: Applies to src/mcp/toolRegistry.ts : Map MCP tool names to handler functions in src/mcp/toolRegistry.ts (registry-based dispatch)

Applied to files:

• src/mcp/toolRegistry.ts

📚 Learning: 2025-10-07T19:31:32.730Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/endpoint-routing.mdc:0-0
Timestamp: 2025-10-07T19:31:32.730Z
Learning: Applies to src/mcp/tools.ts : Register new tool definitions with schema in src/mcp/tools.ts

Applied to files:

• src/mcp/toolRegistry.ts
• src/mcp/tools.ts

📚 Learning: 2025-10-07T19:31:32.730Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/endpoint-routing.mdc:0-0
Timestamp: 2025-10-07T19:31:32.730Z
Learning: Applies to src/index.ts : Extend handleMCPTools switch-case to add new intelligence tool routes

Applied to files:

• src/mcp/toolRegistry.ts
• src/mcp/tools.ts

📚 Learning: 2025-10-07T19:32:40.879Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/mcp-integration.mdc:0-0
Timestamp: 2025-10-07T19:32:40.879Z
Learning: Applies to src/mcp/handlers/**/*.ts : Place each new MCP tool handler in src/mcp/handlers as its own module exporting an async function that returns MCPToolResult and uses MCPEnv

Applied to files:

• src/mcp/toolRegistry.ts
• src/mcp/tools.ts

📚 Learning: 2025-10-08T04:36:05.383Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: CLAUDE.md:0-0
Timestamp: 2025-10-08T04:36:05.383Z
Learning: Applies to src/mcp/tools.ts : Define MCP tool schemas, descriptions, and parameters in src/mcp/tools.ts

Applied to files:

• src/mcp/toolRegistry.ts
• src/mcp/tools.ts

📚 Learning: 2025-10-08T04:34:43.736Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursorrules:0-0
Timestamp: 2025-10-08T04:34:43.736Z
Learning: Applies to src/mcp/handlers/**/*.ts : MCP handlers must return MCPToolResult format

Applied to files:

• src/mcp/toolRegistry.ts

📚 Learning: 2025-10-07T19:32:40.879Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/mcp-integration.mdc:0-0
Timestamp: 2025-10-07T19:32:40.879Z
Learning: Applies to src/mcp/handlers/**/*.ts : Always return isError: true in MCPToolResult for failure cases within handlers

Applied to files:

• src/mcp/toolRegistry.ts

📚 Learning: 2025-10-08T04:34:43.736Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursorrules:0-0
Timestamp: 2025-10-08T04:34:43.736Z
Learning: Applies to src/mcp/tools.ts : Define MCP tools and schemas in src/mcp/tools.ts

Applied to files:

• src/mcp/tools.ts

📚 Learning: 2025-10-07T19:32:40.879Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/mcp-integration.mdc:0-0
Timestamp: 2025-10-07T19:32:40.879Z
Learning: Applies to src/mcp/tools.ts : Provide clear, user-facing descriptions for each tool definition

Applied to files:

• src/mcp/tools.ts

📚 Learning: 2025-10-07T19:32:40.879Z

Learnt from: CR
PR: brendadeeznuts1111/betting-brain-v3#0
File: .cursor/rules/mcp-integration.mdc:0-0
Timestamp: 2025-10-07T19:32:40.879Z
Learning: Applies to src/mcp/tools.ts : Define each new tool in src/mcp/tools.ts with name, clear description, and a JSON Schema inputSchema including required and optional properties

Applied to files:

• src/mcp/tools.ts

🧬 Code graph analysis (7)

src/mcp/handlers/aiSteamDetection.ts (4)

src/types/api.ts (1)

• Env (15-44)

src/mcp/types.ts (1)

• MCPToolResult (56-64)

src/ai/types.ts (1)

• LineMovementData (125-133)

src/ai/betting-analyzer.ts (1)

• BettingAnalyzer (24-320)

src/api/ai-chat.ts (3)

src/ai/types.ts (1)

• AIChatMessage (76-79)

src/types/api.ts (1)

• Env (15-44)

src/utils/request.ts (1)

• CORS_HEADERS (79-84)

src/mcp/handlers/aiRiskReport.ts (3)

src/types/api.ts (1)

• Env (15-44)

src/mcp/types.ts (1)

• MCPToolResult (56-64)

src/ai/types.ts (1)

• ExposureData (138-144)

src/mcp/toolRegistry.ts (3)

src/mcp/handlers/aiSharpAnalysis.ts (1)

• getAISharpAnalysis (11-74)

src/mcp/handlers/aiSteamDetection.ts (1)

• getAISteamDetection (11-72)

src/mcp/handlers/aiRiskReport.ts (1)

• getAIRiskReport (11-70)

src/index.ts (1)

src/api/ai-chat.ts (1)

• handleAIChat (118-235)

src/mcp/handlers/aiSharpAnalysis.ts (4)

src/types/api.ts (1)

• Env (15-44)

src/mcp/types.ts (1)

• MCPToolResult (56-64)

src/ai/types.ts (1)

• CustomerData (113-120)

src/ai/betting-analyzer.ts (1)

• BettingAnalyzer (24-320)

src/mcp/tools.ts (1)

src/mcp/types.ts (1)

• MCPTool (41-49)

🪛 GitHub Actions: cursor-rules-check

src/index.ts

[error] 1-1: Permission denied

🔇 Additional comments (4)

src/mcp/handlers/aiRiskReport.ts (1)

48-60: Remove usage/cost access – generateRiskReport doesn’t return them.

BettingAnalyzer.generateRiskReport() yields risk metrics only; it has no usage/cost fields. Accessing result.usage/result.cost will throw at runtime. Drop those properties (or extend the analyzer to supply them first) before shipping.
[raise_critical-issue]

src/mcp/toolRegistry.ts (2)

26-30: AI tool handler imports added correctly.

Imports align with new handlers and registry usage. Based on learnings.

---

204-204: Good: AI tools initialized in registry.

Ensures tools are available on first call. Based on learnings.

src/mcp/tools.ts (1)

30-32: AI tool list inclusion looks correct.

getAITools() is properly merged into the master tool list.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Relocate doc under /docs per repo policy.

Repository guidelines forbid new Markdown files in the root (only README.md, LICENSE, CLAUDE.md allowed). Please move this document into docs/ (e.g., docs/branching/BRANCHING_STRATEGY.md) and update any references.

🤖 Prompt for AI Agents

BRANCHING_STRATEGY.md lines 1-157: The file is placed in the repo root but repo
policy only allows README.md, LICENSE, CLAUDE.md at root; move this Markdown
into the docs directory (suggested path: docs/branching/BRANCHING_STRATEGY.md),
update any internal links or CI/docs references that point to the old root path
to the new path, and ensure the new file is added to the repository and the root
copy removed; then run any doc build or link-checker used in CI to verify
references resolve.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Include requestId in responses and stop leaking stack traces.

API guidelines require adding the generated requestId to every success/error payload returned from /api/ai/chat. The current response omits it, breaking tracing. Additionally, the error branch serializes error.stack, which exposes internal details to clients and violates our secure-error policy. Please add requestId to the JSON body and drop stack traces (log them server-side only).

🤖 Prompt for AI Agents

In src/api/ai-chat.ts around lines 208 to 234, the responses don’t include the
generated requestId and the error payload leaks error.stack to clients; modify
the success and error JSON bodies to include requestId, and remove/omit the
stack trace from the error response (only log the full error server-side via
console.error). Ensure the AIChatResponse and ErrorResponse objects add
requestId: requestId, and that the error response only returns a safe
message/code/timestamp (no details.stack) while keeping server-side logging of
the error for diagnostics.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Guard /api/ai/chat with cost cap logic.

Per routing rules we must enforce the cost guard from src/guards/costCap.ts on costed AI endpoints. Please apply the guard (e.g., wrap the handler call or early-reject when quotas are exceeded) before delegating to handleAIChat, otherwise runaway usage bypasses the mandated protections.

🤖 Prompt for AI Agents

In src/index.ts around lines 282 to 286, the AI chat route is not protected by
the cost cap guard; import and invoke the cost cap guard from
src/guards/costCap.ts before calling handleAIChat and reject early when quotas
are exceeded. Specifically, call the guard with the request/environment info
right after logging; if the guard indicates quota exceeded, return an
appropriate HTTP error response (e.g., 429 or 402) and do not call handleAIChat,
otherwise proceed to call handleAIChat(request, env).

[coderabbitai]

🛠️ Refactor suggestion | 🟠 Major

Use MCPEnv and add Zod for input validation.

Handlers should use MCPEnv and validate args with Zod before DB queries. Add requestId threading.

As per coding guidelines

-import { Env } from '../../types/api';
+import { MCPEnv } from '../../types/api';
 import { MCPToolResult } from '../types';
 import { BettingAnalyzer } from '../../ai/betting-analyzer';
 import { CustomerData } from '../../ai/types';
+import { z } from 'zod';

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	import { Env } from '../../types/api';
	import { MCPToolResult } from '../types';
	import { BettingAnalyzer } from '../../ai/betting-analyzer';
	import { CustomerData } from '../../ai/types';
	import { MCPEnv } from '../../types/api';
	import { MCPToolResult } from '../types';
	import { BettingAnalyzer } from '../../ai/betting-analyzer';
	import { CustomerData } from '../../ai/types';
	import { z } from 'zod';

🤖 Prompt for AI Agents

In src/mcp/handlers/aiSharpAnalysis.ts around lines 6 to 9, the handler
currently imports Env and lacks input validation and requestId propagation;
replace Env with MCPEnv, validate incoming args using a Zod schema before any DB
queries or business logic, and throw/return early on schema failures; ensure a
requestId is accepted (or generated) and threaded through all calls (DB queries,
BettingAnalyzer instantiation/use, and logging) so downstream ops receive it for
tracing.

[coderabbitai]

🛠️ Refactor suggestion | 🟠 Major

Add requestId, Zod validation, and structured logging.

Generate requestId (or accept from args), validate inputs, and log with requestId + duration.

As per coding guidelines

-export async function getAISharpAnalysis(args: Record<string, any>, env: Env): Promise<MCPToolResult> {
-  try {
-    const { customerId, agentID = 'DEMO' } = args;
+export async function getAISharpAnalysis(args: Record<string, any>, env: MCPEnv): Promise<MCPToolResult> {
+  const incomingReqId = typeof (args as any)?.requestId === 'string' ? (args as any).requestId : undefined;
+  const requestId = incomingReqId ?? Date.now().toString(36);
+  const startedAt = Date.now();
+  try {
+    const ArgsSchema = z.object({
+      customerId: z.string().min(1, 'customerId is required'),
+      agentID: z.string().default('DEMO'),
+      requestId: z.string().optional(),
+    });
+    const parsed = ArgsSchema.safeParse(args);
+    if (!parsed.success) {
+      return {
+        content: [
+          {
+            type: 'text',
+            text: JSON.stringify(
+              { error: 'Invalid arguments', issues: parsed.error.flatten(), tool: 'aiSharpAnalysis', requestId, timestamp: new Date().toISOString() },
+              null,
+              2
+            ),
+          },
+        ],
+        isError: true,
+      };
+    }
+    const { customerId, agentID } = parsed.data;

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	export async function getAISharpAnalysis(args: Record<string, any>, env: Env): Promise<MCPToolResult> {
	try {
	const { customerId, agentID = 'DEMO' } = args;
	if (!customerId) {
	throw new Error('customerId is required');
	}
	export async function getAISharpAnalysis(args: Record<string, any>, env: MCPEnv): Promise<MCPToolResult> {
	const incomingReqId = typeof (args as any)?.requestId === 'string' ? (args as any).requestId : undefined;
	const requestId = incomingReqId ?? Date.now().toString(36);
	const startedAt = Date.now();
	try {
	const ArgsSchema = z.object({
	customerId: z.string().min(1, 'customerId is required'),
	agentID: z.string().default('DEMO'),
	requestId: z.string().optional(),
	});
	const parsed = ArgsSchema.safeParse(args);
	if (!parsed.success) {
	return {
	content: [
	{
	type: 'text',
	text: JSON.stringify(
	{
	error: 'Invalid arguments',
	issues: parsed.error.flatten(),
	tool: 'aiSharpAnalysis',
	requestId,
	timestamp: new Date().toISOString(),
	},
	null,
	2
	),
	},
	],
	isError: true,
	};
	}
	const { customerId, agentID } = parsed.data;

[coderabbitai]

🛠️ Refactor suggestion | 🟠 Major

Structure error with requestId and duration; avoid raw error logging.

Include requestId in logs and response; keep details out of user-visible text.

As per coding guidelines

-  } catch (error) {
-    console.error('AI Sharp Analysis error:', error);
-    return {
-      content: [{ type: 'text', text: JSON.stringify({ error: error instanceof Error ? error.message : 'Unknown error', tool: 'aiSharpAnalysis', agentID: args.agentID || 'DEMO', timestamp: new Date().toISOString() }, null, 2) }],
-      isError: true,
-    };
-  }
+  } catch (error) {
+    const durationMs = Date.now() - startedAt;
+    console.error(`[${requestId}] aiSharpAnalysis error`, { error, durationMs });
+    return {
+      content: [
+        {
+          type: 'text',
+          text: JSON.stringify(
+            {
+              error: error instanceof Error ? error.message : 'Unknown error',
+              tool: 'aiSharpAnalysis',
+              agentID: (args as any)?.agentID ?? 'DEMO',
+              requestId,
+              timestamp: new Date().toISOString(),
+            },
+            null,
+            2
+          ),
+        },
+      ],
+      isError: true,
+    };
+  }

🤖 Prompt for AI Agents

In src/mcp/handlers/aiSharpAnalysis.ts around lines 68-73, replace the raw
console.error and the user-visible JSON.stringify of the error with a structured
log that includes requestId and duration, and return a generic error message (no
raw error details) plus requestId and duration. Specifically: obtain or generate
requestId (args.requestId || create one), compute durationMs from the handler
start time (Date.now() - startTime), log the error in a structured way including
requestId, durationMs, agentID, and the error stack/message (for server logs
only), and return content containing a non-sensitive error text and an object
with requestId and durationMs (no full error message or stack) and isError:
true.

[coderabbitai]

⚠️ Potential issue | 🔴 Critical

Don’t read usage/cost from analyzeSteamMove.

BettingAnalyzer.analyzeSteamMove() returns the detection payload only; it doesn’t expose usage or cost. Attempting to read those fields will crash the handler. Remove or backfill them by updating the analyzer first.

🤖 Prompt for AI Agents

In src/mcp/handlers/aiSteamDetection.ts around lines 50 to 61, the handler is
reading result.usage and result.cost which do not exist on the payload returned
by BettingAnalyzer.analyzeSteamMove and will crash; either remove the aiUsage
property from the response object (omit
inputTokens/outputTokens/totalTokens/cost) or update
BettingAnalyzer.analyzeSteamMove to include a usage and cost object with those
fields before reading them here; implement one of these fixes and ensure the
response still validates (if removing, return no aiUsage or set it to
null/empty; if updating the analyzer, populate usage and cost with numeric
values).

[coderabbitai]

Actionable comments posted: 2

🧹 Nitpick comments (2)

.husky/pre-commit (1)

13-14: Commented-out tests and linting - verify the timeline for enabling these.

Integration tests and linting are currently disabled. While the comments indicate these are temporary ("no tests yet", "will add in next commit"), these are essential quality gates that should be prioritized.

Consider creating GitHub issues to track:

1. Adding integration tests for pre-commit
2. Enabling linting in the pre-commit hook

Would you like me to help draft issue descriptions for tracking these tasks?

Also applies to: 19-20

PHASE_3_COMPLETE.md (1)

16-16: Optional: Consider using proper heading syntax instead of emphasis.

Multiple lines use bold emphasis (**text**) where Markdown headings might be more semantically appropriate. While this is a stylistic choice and the current formatting is readable, using proper headings (e.g., ### AI Chat API Endpoint) would improve document structure and SEO.

For example, on Line 16:

-**AI Chat API Endpoint**
+### AI Chat API Endpoint

This is flagged by markdownlint (MD036) but is entirely optional - the current format is acceptable if you prefer the visual style.

Also applies to: 63-63, 103-103, 145-145, 186-186, 200-200, 221-221, 246-246

📜 Review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 558fa44 and a9b56af.

📒 Files selected for processing (5)

• .editorconfig (1 hunks)
• .eslintrc.json (1 hunks)
• .husky/pre-commit (1 hunks)
• .prettierrc.json (1 hunks)
• PHASE_3_COMPLETE.md (1 hunks)

✅ Files skipped from review due to trivial changes (1)

• .editorconfig

🧰 Additional context used

📓 Path-based instructions (5)

!(README|CLAUDE).md

📄 CodeRabbit inference engine (.cursor/rules/root-organization.mdc)

Do not place Markdown files in the root except README.md and CLAUDE.md

Files:

• .husky/pre-commit
• .prettierrc.json
• .eslintrc.json
• PHASE_3_COMPLETE.md

**/*.md

📄 CodeRabbit inference engine (.cursor/rules/documentation.mdc)

All documentation Markdown files must be placed under docs/ (only README.md, LICENSE, and CLAUDE.md may live in the repository root)

Files:

• PHASE_3_COMPLETE.md

*.md

📄 CodeRabbit inference engine (.cursor/rules/documentation.mdc)

Do not create Markdown documentation files in the repository root except README.md, LICENSE, and CLAUDE.md

Files:

• PHASE_3_COMPLETE.md

**/*

📄 CodeRabbit inference engine (.cursor/rules/code-searchability.mdc)

Avoid hardcoded URLs in code; reference configuration/constants instead

Files:

• PHASE_3_COMPLETE.md

*

📄 CodeRabbit inference engine (.cursor/rules/root-organization.mdc)

Only these files are permitted in the repository root: README.md, LICENSE, CLAUDE.md, package.json, package-lock.json, bun.lock, tsconfig.json, wrangler.toml, wrangler.staging.toml, wrangler.production.toml

Files:

• PHASE_3_COMPLETE.md

🪛 GitHub Actions: cursor-rules-check

PHASE_3_COMPLETE.md

[error] 1-1: PHASE_3_COMPLETE.md: command not found.

🪛 markdownlint-cli2 (0.18.1)

PHASE_3_COMPLETE.md

16-16: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

63-63: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

103-103: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

145-145: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

186-186: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

200-200: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

221-221: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

246-246: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

🔇 Additional comments (2)

.husky/pre-commit (2)

6-11: LGTM! Formatting check implementation looks solid.

The formatting check properly fails fast with a clear error message and exit code, guiding developers to run bun run fmt to fix issues.

---

17-17: Clarify security-check behavior in pre-commit hook
Line 17 (bun run security || echo "⚠️ Security check skipped") currently silences failures. Choose one:

• Enforce checks by failing the commit:

  - bun run security || echo "⚠️  Security check skipped"
  + echo "🔒 Running security checks…"
  + bun run security || {
  +   echo "❌ Security check failed. Please address security issues before committing."
  +   exit 1
  + }

• Keep skips but document intent:

  - bun run security || echo "⚠️  Security check skipped"
  + # Security checks are optional during development
  + bun run security || echo "⚠️  Security check skipped"

[coderabbitai]

⚠️ Potential issue | 🔴 Critical

Fix the corrupted emoji character.

The echo statement contains a corrupted Unicode character �� instead of a proper emoji. This is likely due to an encoding issue and will display incorrectly in terminals.

Apply this diff to fix the encoding issue:

-echo "�� Running pre-commit checks..."
+echo "🔍 Running pre-commit checks..."

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	echo "�� Running pre-commit checks..."
	echo "🔍 Running pre-commit checks..."

🤖 Prompt for AI Agents

.husky/pre-commit line 4: the echo statement contains a corrupted Unicode glyph
("��"); replace it with a valid emoji or plain ASCII text (for example "🔧" or
"[pre-commit]") and ensure the file is saved with UTF-8 encoding (no BOM) so
terminals display it correctly; update the echo line to use the chosen valid
character or text and commit the file.

[coderabbitai]

⚠️ Potential issue | 🔴 Critical

Move documentation to docs/ directory per coding guidelines.

This file violates the project's coding guidelines, which state: "All documentation Markdown files must be placed under docs/ (only README.md, LICENSE, and CLAUDE.md may live in the repository root)."

Additionally, the pipeline failure PHASE_3_COMPLETE.md: command not found suggests the file location is causing issues with the cursor-rules-check workflow.

As per coding guidelines.

Apply this fix:

#!/bin/bash
# Move the file to the correct location
mkdir -p docs
git mv PHASE_3_COMPLETE.md docs/PHASE_3_COMPLETE.md

Then update any references to this file in other documentation or scripts to point to docs/PHASE_3_COMPLETE.md.

🧰 Tools

🪛 GitHub Actions: cursor-rules-check

[error] 1-1: PHASE_3_COMPLETE.md: command not found.

🪛 markdownlint-cli2 (0.18.1)

16-16: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

63-63: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

103-103: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

145-145: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

186-186: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

200-200: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

221-221: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

---

246-246: Emphasis used instead of a heading

(MD036, no-emphasis-as-heading)

🤖 Prompt for AI Agents

In PHASE_3_COMPLETE.md around lines 1-337 the documentation lives in the
repository root which violates the guideline that all markdown docs (except
README.md, LICENSE, CLAUDE.md) must live under docs/ and is triggering the
cursor-rules-check pipeline error; move the file into docs/ by creating the docs
directory if missing and performing a git mv PHASE_3_COMPLETE.md
docs/PHASE_3_COMPLETE.md, then update any references to PHASE_3_COMPLETE.md in
documentation, scripts, CI/workflows or README files to point to
docs/PHASE_3_COMPLETE.md and commit the change so the pipeline no longer treats
the file as a misplaced executable.

[coderabbitai]

Actionable comments posted: 4

📜 Review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between a9b56af and 2d83ae7.

📒 Files selected for processing (3)

• src/api/ai-health.ts (1 hunks)
• src/api/ai-query.ts (1 hunks)
• src/index.ts (2 hunks)

🚧 Files skipped from review as they are similar to previous changes (1)

• src/index.ts

🧰 Additional context used

📓 Path-based instructions (11)

src/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/cloudflare-workers.mdc)

Use the Env, BetTickerSnifferEnv, and MCPEnv types from src/types/api.ts across TypeScript sources

Use @cloudflare/workers-types for Cloudflare Worker TypeScript definitions across source files

src/**/*.ts: Always use parameterized queries with .bind() for all D1 queries
Never use string interpolation in SQL for D1 queries
Always cast D1 results with as unknown as <Type> when consuming D1Result
Always limit query results (use LIMIT) in D1 queries
Always handle database errors around D1 operations (try/catch and propagate meaningful errors)
Never forget to bind parameters when executing D1 queries
Never ignore database errors from D1 operations

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*.{ts,tsx,js,jsx}

📄 CodeRabbit inference engine (.cursorrules)

**/*.{ts,tsx,js,jsx}: Use Bun runtime and tooling: bun commands only; use Bun file APIs; leverage Bun native APIs; and use processManager for spawning
Never use parseFloat() for stakes; use Number() instead
Use explicit UTC timestamps instead of new Date() defaults

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*.{ts,tsx}

📄 CodeRabbit inference engine (.cursorrules)

**/*.{ts,tsx}: Use standardized error classes by importing from src/utils/error-handler.ts
Always use try-catch or an asyncHandler wrapper around async request handlers
Return structured error responses and avoid exposing internal details
Always cast D1 results with as unknown as when necessary
Always handle database errors
Always validate all inputs using Zod schemas before processing
Always free WASM memory in finally blocks
Always generate a requestId (Date.now().toString(36)) and prefix logs with it
Always include CORS headers and handle OPTIONS preflight for HTTP endpoints
Always use processManager.spawn() with timeouts and cleanup in finally; never leave zombie processes

**/*.{ts,tsx}: Never use string interpolation in SQL queries; always use parameterized queries with placeholders and .bind(...)
All API responses must include CORS headers (e.g., Access-Control-Allow-Origin) when returning Response objects
All console logs should be structured to include the requestId prefix

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*.{ts,tsx,sql}

📄 CodeRabbit inference engine (.cursorrules)

Database queries must be parameterized (use .bind()), never string interpolation

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*.{ts,tsx,jsx,js}

📄 CodeRabbit inference engine (.cursorrules)

Always sanitize output to prevent XSS; never trust user input

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

src/api/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/api-patterns.mdc)

src/api/**/*.ts: In endpoint code, throw typed errors via Errors.* and format failures with createErrorResponse(error, requestId, path)
Use specific Errors.* types to map to correct HTTP codes (badRequest, unauthorized, forbidden, notFound, validationError, rateLimit, databaseError, serviceUnavailable, timeout)
Prefer wrapping handlers with asyncHandler(...) so thrown errors are auto-caught and formatted
At the start of each handler, validate required environment variables with validateEnv(env, [...])
Validate incoming input using validateQueryParams/Validators and return validationErrorResponse on failure
Use parameterized D1 queries with .prepare(...).bind(...).all() and never interpolate variables into SQL strings
When reading D1 results, cast results via unknown as needed to strongly-typed arrays/objects
Always include standard CORS headers in responses: Access-Control-Allow-Origin '*', Content-Type 'application/json' (and methods/headers as needed)
Generate a requestId at the start of each request (Date.now().toString(36)), log with it, include it in success payloads and in createErrorResponse
Structure success responses as JSON with data payload plus metadata: requestId and timestamp; include pagination fields when applicable
Log appropriately per request: incoming, success summaries, and errors, all prefixed with [requestId]

All endpoint handlers must generate and use a requestId (e.g., include requestId: string in handler signature and use it in logs)

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*.{ts,tsx,js,mjs,cjs}

📄 CodeRabbit inference engine (.cursor/rules/bun-runtime.mdc)

**/*.{ts,tsx,js,mjs,cjs}: Use the shared process manager (import processManager from './tests/utils/process-cleanup') for all process spawning
Do not call Bun.spawn() directly
Do not use Node.js child_process for spawning
Use Bun file APIs (Bun.file, Bun.write, etc.) instead of Node.js fs or fs/promises
Prefer Bun’s native APIs (crypto, HTTP client, streams, etc.) over external libraries when those features are needed

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*

📄 CodeRabbit inference engine (.cursor/rules/code-searchability.mdc)

Avoid hardcoded URLs in code; reference configuration/constants instead

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

**/*.{ts,tsx,js,jsx,mjs,cjs}

📄 CodeRabbit inference engine (.cursor/rules/production-security.mdc)

**/*.{ts,tsx,js,jsx,mjs,cjs}: Do not use parseFloat() for betting stakes; use Number() and validate with isNaN() and > 0 before accepting input
For Cloudflare D1 queries, never interpolate values into SQL strings; use parameterized queries with .prepare(...).bind(...)
When sending to Cloudflare Queues, limit each batch to a maximum of 100 messages (chunk sends accordingly)
Avoid using new Date() without arguments in Edge Workers; prefer Date.now() or explicit UTC (e.g., toISOString())
Manage WASM memory explicitly: allocate, use, and free in a finally block to prevent leaks

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

!(README|CLAUDE).md

📄 CodeRabbit inference engine (.cursor/rules/root-organization.mdc)

Do not place Markdown files in the root except README.md and CLAUDE.md

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

{src,scripts}/**/*.ts

📄 CodeRabbit inference engine (.cursor/rules/security-patterns.mdc)

{src,scripts}/**/*.ts: Always validate all inputs before use (type, length, bounds)
Always use parameterized queries; never interpolate variables into SQL strings
Always sanitize output to prevent XSS when rendering or embedding user-controlled data
Always validate JWT or auth tokens (verify signature and expiration) before trusting them
Always implement rate limiting for request handling paths
Always hash sensitive data (e.g., passwords) using strong algorithms (e.g., bcrypt) before storage
Always use secure, generic error messages; log details internally only
Always sanitize/redact sensitive fields (password, token, apiKey) in logs
Always use restrictive CORS configuration (specific origins, methods, headers)
Always validate required environment variables before use
Never expose internal error details (messages, stacks) in HTTP responses
Never hardcode secrets; load secrets from environment or secret manager and validate presence
Never omit or ignore standard security headers in responses (e.g., HSTS, X-Frame-Options, CSP, X-Content-Type-Options)
Use authenticated encryption for sensitive data at rest/in transit (e.g., AES-GCM)
Validate uploaded files (MIME type whitelist and maximum size)
Prevent path traversal by validating and normalizing file paths; reject paths with '..', absolute roots, or excessive length
Set standard security headers on all HTTP responses (e.g., HSTS, CSP, X-Frame-Options, X-Content-Type-Options, X-XSS-Protection)

Files:

• src/api/ai-health.ts
• src/api/ai-query.ts

🧬 Code graph analysis (2)

src/api/ai-health.ts (2)

src/types/api.ts (1)

• Env (15-44)

src/utils/request.ts (1)

• CORS_HEADERS (79-84)

src/api/ai-query.ts (6)

src/types/api.ts (1)

• Env (15-44)

src/mcp/handlers/aiSharpAnalysis.ts (1)

• getAISharpAnalysis (11-74)

src/mcp/handlers/aiSteamDetection.ts (1)

• getAISteamDetection (11-72)

src/mcp/handlers/aiRiskReport.ts (1)

• getAIRiskReport (11-70)

src/ai/betting-analyzer.ts (1)

• BettingAnalyzer (24-320)

src/utils/request.ts (1)

• CORS_HEADERS (79-84)

🔇 Additional comments (1)

src/api/ai-health.ts (1)

24-69: LGTM - Helper functions are well-implemented.

The checkDatabase and checkKimiAPI helper functions properly handle errors and return appropriate status values. The database check uses a safe query to count tables, and the Kimi API check validates the API key before making the request.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Add requestId and follow endpoint guidelines.

The main handler is missing several required elements per coding guidelines:

1. No requestId generation: All endpoint handlers must generate and use a requestId (e.g., Date.now().toString(36)).
2. Logs missing requestId prefix: Lines 75, 108, 115 should include [${requestId}] prefix.
3. Response missing requestId: Lines 100-106 (success) and 117-122 (error) should include requestId in the payload.
4. No environment validation: Should use validateEnv(env, ['ANALYTICS', 'RAW_FEED_DB', 'KIMI_API_KEY']) at the start.
5. No asyncHandler wrapper: Consider wrapping the handler for consistent error formatting.

Apply these changes:

 export async function handleHealthCheck(request: Request, env: Env): Promise<Response> {
+  const requestId = Date.now().toString(36);
-  console.log('🏥 Health check request received');
+  console.log(`[${requestId}] 🏥 Health check request received`);
 
   try {
     // Check Kimi API
     const kimiStatus = await checkKimiAPI(env.KIMI_API_KEY);
 
     // Check databases
     const databases = await Promise.all([
       checkDatabase(env.ANALYTICS, 'ANALYTICS'),
       checkDatabase(env.RAW_FEED_DB, 'RAW_FEED_DB'),
     ]);
 
     // Determine overall status
     const allDbsConnected = databases.every(db => db.status === 'connected');
     const kimiConnected = kimiStatus === 'connected';
 
     let status: 'ok' | 'degraded' | 'error';
     if (kimiConnected && allDbsConnected) {
       status = 'ok';
     } else if (kimiConnected || allDbsConnected) {
       status = 'degraded';
     } else {
       status = 'error';
     }
 
     const response: HealthCheckResponse = {
       status,
       kimi: kimiStatus,
       databases,
+      requestId,
       timestamp: new Date().toISOString(),
       version: '3.0.0-phase3',
     };
 
-    console.log('✅ Health check complete:', status);
+    console.log(`[${requestId}] ✅ Health check complete:`, status);
 
     return new Response(JSON.stringify(response, null, 2), {
       status: status === 'ok' ? 200 : status === 'degraded' ? 206 : 503,
       headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
     });
   } catch (error) {
-    console.error('❌ Health check error:', error);
+    console.error(`[${requestId}] ❌ Health check error:`, error);
 
     const errorResponse: HealthCheckResponse = {
       status: 'error',
       kimi: 'disconnected',
       databases: [],
+      requestId,
       timestamp: new Date().toISOString(),
       version: '3.0.0-phase3',
     };
 
     return new Response(JSON.stringify(errorResponse, null, 2), {
       status: 503,
       headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
     });
   }
 }

Also update the HealthCheckResponse interface:

 interface HealthCheckResponse {
   status: 'ok' | 'degraded' | 'error';
   kimi: 'connected' | 'disconnected' | 'not_configured';
   databases: {
     name: string;
     status: 'connected' | 'error';
     tables?: number;
   }[];
+  requestId: string;
   timestamp: string;
   version: string;
 }

As per coding guidelines: "All endpoint handlers must generate and use a requestId", "All console logs should be structured to include the requestId prefix", and "Structure success responses as JSON with data payload plus metadata: requestId and timestamp".

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	export async function handleHealthCheck(request: Request, env: Env): Promise<Response> {
	console.log('🏥 Health check request received');
	try {
	// Check Kimi API
	const kimiStatus = await checkKimiAPI(env.KIMI_API_KEY);
	// Check databases
	const databases = await Promise.all([
	checkDatabase(env.ANALYTICS, 'ANALYTICS'),
	checkDatabase(env.RAW_FEED_DB, 'RAW_FEED_DB'),
	]);
	// Determine overall status
	const allDbsConnected = databases.every(db => db.status === 'connected');
	const kimiConnected = kimiStatus === 'connected';
	let status: 'ok' | 'degraded' | 'error';
	if (kimiConnected && allDbsConnected) {
	status = 'ok';
	} else if (kimiConnected || allDbsConnected) {
	status = 'degraded';
	} else {
	status = 'error';
	}
	const response: HealthCheckResponse = {
	status,
	kimi: kimiStatus,
	databases,
	timestamp: new Date().toISOString(),
	version: '3.0.0-phase3',
	};
	console.log('✅ Health check complete:', status);
	return new Response(JSON.stringify(response, null, 2), {
	status: status === 'ok' ? 200 : status === 'degraded' ? 206 : 503,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	} catch (error) {
	console.error('❌ Health check error:', error);
	const errorResponse: HealthCheckResponse = {
	status: 'error',
	kimi: 'disconnected',
	databases: [],
	timestamp: new Date().toISOString(),
	version: '3.0.0-phase3',
	};
	return new Response(JSON.stringify(errorResponse, null, 2), {
	status: 503,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	}
	export async function handleHealthCheck(request: Request, env: Env): Promise<Response> {
	const requestId = Date.now().toString(36);
	console.log(`[${requestId}] 🏥 Health check request received`);
	try {
	// Check Kimi API
	const kimiStatus = await checkKimiAPI(env.KIMI_API_KEY);
	// Check databases
	const databases = await Promise.all([
	checkDatabase(env.ANALYTICS, 'ANALYTICS'),
	checkDatabase(env.RAW_FEED_DB, 'RAW_FEED_DB'),
	]);
	// Determine overall status
	const allDbsConnected = databases.every(db => db.status === 'connected');
	const kimiConnected = kimiStatus === 'connected';
	let status: 'ok' | 'degraded' | 'error';
	if (kimiConnected && allDbsConnected) {
	status = 'ok';
	} else if (kimiConnected || allDbsConnected) {
	status = 'degraded';
	} else {
	status = 'error';
	}
	const response: HealthCheckResponse = {
	status,
	kimi: kimiStatus,
	databases,
	requestId,
	timestamp: new Date().toISOString(),
	version: '3.0.0-phase3',
	};
	console.log(`[${requestId}] ✅ Health check complete:`, status);
	return new Response(JSON.stringify(response, null, 2), {
	status: status === 'ok' ? 200 : status === 'degraded' ? 206 : 503,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	} catch (error) {
	console.error(`[${requestId}] ❌ Health check error:`, error);
	const errorResponse: HealthCheckResponse = {
	status: 'error',
	kimi: 'disconnected',
	databases: [],
	requestId,
	timestamp: new Date().toISOString(),
	version: '3.0.0-phase3',
	};
	return new Response(JSON.stringify(errorResponse, null, 2), {
	status: 503,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	}

🤖 Prompt for AI Agents

In src/api/ai-health.ts around lines 74 to 130: add an initial call to
validateEnv(env, ['ANALYTICS', 'RAW_FEED_DB', 'KIMI_API_KEY']), generate const
requestId = Date.now().toString(36) at the top of the handler, and prefix all
console logs on lines ~75, ~108 and ~115 with [${requestId}]. Include requestId
in both success and error JSON payloads (add requestId alongside status,
timestamp, version and data), and ensure the handler is exported/wrapped with
asyncHandler for consistent error formatting. Also update the
HealthCheckResponse interface to include requestId:string and move the actual
health data into a data field per endpoint guidelines.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Add input validation and fix Date usage.

The function has several issues that should be addressed:

1. Line 87: Uses new Date() without explicit UTC. Per coding guidelines, prefer Date.now() or explicit UTC timestamps instead of new Date() defaults.
2. No input validation: The query string is not validated for length or sanitized before regex operations, which could lead to performance issues or unexpected behavior with malformed input.
3. No error handling: Regex operations could fail or produce unexpected results with certain inputs.

Apply these changes:

 function parseQuery(query: string): { intent: string; params: Record<string, any> } {
+  // Validate input
+  if (!query || query.length > 1000) {
+    throw new Error('Invalid query: must be between 1 and 1000 characters');
+  }
+
   const lowerQuery = query.toLowerCase();
 
   // Sharp customer analysis
   if (lowerQuery.includes('sharp') || lowerQuery.includes('customer') || lowerQuery.includes('cust_')) {
     const customerMatch = query.match(/cust[_-]?(\w+)/i);
     if (customerMatch) {
       return {
         intent: 'sharp_analysis',
         params: { customerId: `CUST_${customerMatch[1]}` },
       };
     }
   }
 
   // Steam move detection
   if (lowerQuery.includes('steam') || lowerQuery.includes('line move') || lowerQuery.includes('movement')) {
     // Try to extract team names
     const teams = query.match(/(\w+)\s+(?:vs?\.?|@)\s+(\w+)/i);
     if (teams) {
       return {
         intent: 'steam_detection',
         params: {
           homeTeam: teams[2],
           awayTeam: teams[1],
         },
       };
     }
     
     // Try to extract event ID
     const eventMatch = query.match(/([A-Z]+_[A-Z0-9_]+)/);
     if (eventMatch) {
       return {
         intent: 'steam_detection',
         params: { eventId: eventMatch[1] },
       };
     }
   }
 
   // Risk report
   if (lowerQuery.includes('risk') || lowerQuery.includes('exposure') || lowerQuery.includes('hedge')) {
     const eventMatch = query.match(/([A-Z]+_[A-Z0-9_]+)/);
     if (eventMatch) {
       return {
         intent: 'risk_report',
         params: { eventId: eventMatch[1] },
       };
     }
     
     // General risk report
     return {
       intent: 'risk_report',
-      params: { date: new Date().toISOString().split('T')[0] },
+      params: { date: new Date(Date.now()).toISOString().split('T')[0] },
     };
   }
 
   // Agent performance
   if (lowerQuery.includes('agent') || lowerQuery.includes('top') || lowerQuery.includes('performance')) {
     return {
       intent: 'agent_performance',
       params: { period: 'week' },
     };
   }
 
   // Default to general chat
   return {
     intent: 'general_chat',
     params: { query },
   };
 }

As per coding guidelines: "Use explicit UTC timestamps instead of new Date() defaults" and "Always validate all inputs before use (type, length, bounds)".

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	function parseQuery(query: string): { intent: string; params: Record<string, any> } {
	const lowerQuery = query.toLowerCase();
	// Sharp customer analysis
	if (lowerQuery.includes('sharp') || lowerQuery.includes('customer') || lowerQuery.includes('cust_')) {
	const customerMatch = query.match(/cust[_-]?(\w+)/i);
	if (customerMatch) {
	return {
	intent: 'sharp_analysis',
	params: { customerId: `CUST_${customerMatch[1]}` },
	};
	}
	}
	// Steam move detection
	if (lowerQuery.includes('steam') || lowerQuery.includes('line move') || lowerQuery.includes('movement')) {
	// Try to extract team names
	const teams = query.match(/(\w+)\s+(?:vs?\.?|@)\s+(\w+)/i);
	if (teams) {
	return {
	intent: 'steam_detection',
	params: {
	homeTeam: teams[2],
	awayTeam: teams[1],
	},
	};
	}
	// Try to extract event ID
	const eventMatch = query.match(/([A-Z]+_[A-Z0-9_]+)/);
	if (eventMatch) {
	return {
	intent: 'steam_detection',
	params: { eventId: eventMatch[1] },
	};
	}
	}
	// Risk report
	if (lowerQuery.includes('risk') || lowerQuery.includes('exposure') || lowerQuery.includes('hedge')) {
	const eventMatch = query.match(/([A-Z]+_[A-Z0-9_]+)/);
	if (eventMatch) {
	return {
	intent: 'risk_report',
	params: { eventId: eventMatch[1] },
	};
	}
	// General risk report
	return {
	intent: 'risk_report',
	params: { date: new Date().toISOString().split('T')[0] },
	};
	}
	// Agent performance
	if (lowerQuery.includes('agent') || lowerQuery.includes('top') || lowerQuery.includes('performance')) {
	return {
	intent: 'agent_performance',
	params: { period: 'week' },
	};
	}
	// Default to general chat
	return {
	intent: 'general_chat',
	params: { query },
	};
	}
	function parseQuery(query: string): { intent: string; params: Record<string, any> } {
	// Validate input
	if (!query || query.length > 1000) {
	throw new Error('Invalid query: must be between 1 and 1000 characters');
	}
	const lowerQuery = query.toLowerCase();
	// Sharp customer analysis
	if (lowerQuery.includes('sharp') || lowerQuery.includes('customer') || lowerQuery.includes('cust_')) {
	const customerMatch = query.match(/cust[_-]?(\w+)/i);
	if (customerMatch) {
	return {
	intent: 'sharp_analysis',
	params: { customerId: `CUST_${customerMatch[1]}` },
	};
	}
	}
	// Steam move detection
	if (lowerQuery.includes('steam') || lowerQuery.includes('line move') || lowerQuery.includes('movement')) {
	// Try to extract team names
	const teams = query.match(/(\w+)\s+(?:vs?\.?|@)\s+(\w+)/i);
	if (teams) {
	return {
	intent: 'steam_detection',
	params: {
	homeTeam: teams[2],
	awayTeam: teams[1],
	},
	};
	}
	// Try to extract event ID
	const eventMatch = query.match(/([A-Z]+_[A-Z0-9_]+)/);
	if (eventMatch) {
	return {
	intent: 'steam_detection',
	params: { eventId: eventMatch[1] },
	};
	}
	}
	// Risk report
	if (lowerQuery.includes('risk') || lowerQuery.includes('exposure') || lowerQuery.includes('hedge')) {
	const eventMatch = query.match(/([A-Z]+_[A-Z0-9_]+)/);
	if (eventMatch) {
	return {
	intent: 'risk_report',
	params: { eventId: eventMatch[1] },
	};
	}
	// General risk report
	return {
	intent: 'risk_report',
	params: { date: new Date(Date.now()).toISOString().split('T')[0] },
	};
	}
	// Agent performance
	if (lowerQuery.includes('agent') || lowerQuery.includes('top') || lowerQuery.includes('performance')) {
	return {
	intent: 'agent_performance',
	params: { period: 'week' },
	};
	}
	// Default to general chat
	return {
	intent: 'general_chat',
	params: { query },
	};
	}

🤖 Prompt for AI Agents

In src/api/ai-query.ts around lines 36 to 104, validate and harden parseQuery:
ensure query is a non-empty string (typeof check, trim, enforce a max length
e.g. 1000 chars) and return a safe default error or general_chat if validation
fails; sanitize/truncate the string before running regexes; wrap the parsing
logic in a try/catch to handle unexpected regex errors and fall back to intent
'general_chat'; guard each regex result with null checks before using capture
groups; and replace the Date usage that returns the current date with an
explicit UTC-based timestamp (e.g. derive the ISO date via Date.now() or new
Date(Date.now()).toISOString().split('T')[0]) so the risk_report date is
generated using an explicit UTC timestamp.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Add error handling for JSON parsing.

The executor functions parse JSON without error handling, which could cause the handler to crash if the result content is malformed:

• Line 116 in executeSharpAnalysis
• Line 136 in executeSteamDetection
• Line 149 in executeRiskReport

Add try-catch around JSON.parse operations:

 async function executeSharpAnalysis(params: Record<string, any>, env: Env): Promise<any> {
   const result = await getAISharpAnalysis(params, env);
   
   if (result.isError) {
     throw new Error(result.content[0].text);
   }
   
-  return JSON.parse(result.content[0].text);
+  try {
+    return JSON.parse(result.content[0].text);
+  } catch (error) {
+    throw new Error(`Failed to parse sharp analysis result: ${error instanceof Error ? error.message : 'Unknown error'}`);
+  }
 }

Apply similar changes to executeSteamDetection (line 136) and executeRiskReport (line 149).

As per coding guidelines: "Always use try-catch or an asyncHandler wrapper around async request handlers".

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	async function executeSharpAnalysis(params: Record<string, any>, env: Env): Promise<any> {
	const result = await getAISharpAnalysis(params, env);
	if (result.isError) {
	throw new Error(result.content[0].text);
	}
	return JSON.parse(result.content[0].text);
	}
	/**
	* Execute steam detection
	*/
	async function executeSteamDetection(params: Record<string, any>, env: Env): Promise<any> {
	// If we have team names, construct event ID
	if (params.homeTeam && params.awayTeam) {
	const today = new Date().toISOString().split('T')[0].replace(/-/g, '');
	params.eventId = `NBA_${params.awayTeam.toUpperCase()}_${params.homeTeam.toUpperCase()}_${today}`;
	params.marketType = 'SPREAD';
	}
	const result = await getAISteamDetection(params, env);
	if (result.isError) {
	throw new Error(result.content[0].text);
	}
	return JSON.parse(result.content[0].text);
	}
	/**
	* Execute risk report
	*/
	async function executeRiskReport(params: Record<string, any>, env: Env): Promise<any> {
	const result = await getAIRiskReport(params, env);
	if (result.isError) {
	throw new Error(result.content[0].text);
	}
	return JSON.parse(result.content[0].text);
	}
	async function executeSharpAnalysis(params: Record<string, any>, env: Env): Promise<any> {
	const result = await getAISharpAnalysis(params, env);
	if (result.isError) {
	throw new Error(result.content[0].text);
	}
	try {
	return JSON.parse(result.content[0].text);
	} catch (error) {
	throw new Error(
	`Failed to parse sharp analysis result: ${
	error instanceof Error ? error.message : 'Unknown error'
	}`
	);
	}
	}

[coderabbitai]

⚠️ Potential issue | 🔴 Critical

Fix multiple guideline violations in main handler.

The main handler has several issues that must be addressed:

1. Line 221: No try-catch around request.json() - malformed JSON will crash the handler before reaching the outer try-catch.
2. Lines 281-288: Missing requestId in success response payload.
3. Lines 297-303: Missing requestId in error response.
4. No asyncHandler wrapper: The handler should use asyncHandler(...) to auto-catch and format errors.
5. No Zod validation: Request body should be validated using a Zod schema.

Apply these fixes:

+import { z } from 'zod';
+
+const QueryRequestSchema = z.object({
+  query: z.string().min(1).max(1000),
+  agentID: z.string().optional(),
+});
+
 export async function handleAIQuery(request: Request, env: Env): Promise<Response> {
   const requestId = Date.now().toString(36);
   console.log(`[${requestId}] 🔍 AI query request received`);
 
   try {
     // Validate API key
     if (!env.KIMI_API_KEY || env.KIMI_API_KEY === 'sk-placeholder-use-dotenv-or-secrets') {
       return new Response(JSON.stringify({ error: 'AI service not configured', requestId, timestamp: new Date().toISOString() }), {
         status: 503,
         headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
       });
     }
 
     // Parse request
-    const body = await request.json() as QueryRequest;
+    let body: QueryRequest;
+    try {
+      const rawBody = await request.json();
+      body = QueryRequestSchema.parse(rawBody);
+    } catch (error) {
+      return new Response(JSON.stringify({ 
+        error: error instanceof Error ? error.message : 'Invalid request body',
+        requestId,
+        timestamp: new Date().toISOString()
+      }), {
+        status: 400,
+        headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
+      });
+    }
     
-    if (!body.query) {
-      return new Response(JSON.stringify({ error: 'query is required' }), {
-        status: 400,
-        headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
-      });
-    }
 
     console.log(`[${requestId}] Query: "${body.query}"`);
 
     // Parse intent
     const { intent, params } = parseQuery(body.query);
     console.log(`[${requestId}] Intent: ${intent}`, params);
 
     // Add agentID if provided
     if (body.agentID) {
       params.agentID = body.agentID;
     }
 
     // Execute based on intent
     let result: any;
     let usage: any;
     let cost: any;
 
     switch (intent) {
       case 'sharp_analysis':
         result = await executeSharpAnalysis(params, env);
         usage = result.aiUsage;
         cost = { totalCost: result.aiUsage?.cost };
         break;
 
       case 'steam_detection':
         result = await executeSteamDetection(params, env);
         usage = result.aiUsage;
         cost = { totalCost: result.aiUsage?.cost };
         break;
 
       case 'risk_report':
         result = await executeRiskReport(params, env);
         usage = result.aiUsage;
         cost = { totalCost: result.aiUsage?.cost };
         break;
 
       case 'agent_performance':
         result = await executeAgentPerformance(params, env);
         break;
 
       case 'general_chat':
         result = await executeGeneralChat(params, env);
         usage = result.usage;
         cost = result.cost;
         break;
 
       default:
         throw new Error(`Unknown intent: ${intent}`);
     }
 
     console.log(`[${requestId}] ✅ Query executed successfully`);
 
     const response: QueryResponse = {
       query: body.query,
       intent,
       result,
       usage,
       cost,
+      requestId,
       timestamp: new Date().toISOString(),
     };
 
     return new Response(JSON.stringify(response, null, 2), {
       status: 200,
       headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
     });
   } catch (error) {
     console.error(`[${requestId}] ❌ Query error:`, error);
 
     return new Response(JSON.stringify({
       error: error instanceof Error ? error.message : 'Unknown error',
+      requestId,
       timestamp: new Date().toISOString(),
     }), {
       status: 500,
       headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
     });
   }
 }

Also update the QueryResponse interface to include requestId:

 interface QueryResponse {
   query: string;
   intent: string;
   result: any;
   usage?: {
     inputTokens: number;
     outputTokens: number;
     totalTokens: number;
   };
   cost?: {
     totalCost: number;
   };
+  requestId: string;
   timestamp: string;
 }

As per coding guidelines: "Always use try-catch or an asyncHandler wrapper around async request handlers", "Always validate all inputs using Zod schemas before processing", "Structure success responses as JSON with data payload plus metadata: requestId and timestamp", and "include it in success payloads and in createErrorResponse".

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	export async function handleAIQuery(request: Request, env: Env): Promise<Response> {
	const requestId = Date.now().toString(36);
	console.log(`[${requestId}] 🔍 AI query request received`);
	try {
	// Validate API key
	if (!env.KIMI_API_KEY || env.KIMI_API_KEY === 'sk-placeholder-use-dotenv-or-secrets') {
	return new Response(JSON.stringify({ error: 'AI service not configured' }), {
	status: 503,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	// Parse request
	const body = await request.json() as QueryRequest;
	if (!body.query) {
	return new Response(JSON.stringify({ error: 'query is required' }), {
	status: 400,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	console.log(`[${requestId}] Query: "${body.query}"`);
	// Parse intent
	const { intent, params } = parseQuery(body.query);
	console.log(`[${requestId}] Intent: ${intent}`, params);
	// Add agentID if provided
	if (body.agentID) {
	params.agentID = body.agentID;
	}
	// Execute based on intent
	let result: any;
	let usage: any;
	let cost: any;
	switch (intent) {
	case 'sharp_analysis':
	result = await executeSharpAnalysis(params, env);
	usage = result.aiUsage;
	cost = { totalCost: result.aiUsage?.cost };
	break;
	case 'steam_detection':
	result = await executeSteamDetection(params, env);
	usage = result.aiUsage;
	cost = { totalCost: result.aiUsage?.cost };
	break;
	case 'risk_report':
	result = await executeRiskReport(params, env);
	usage = result.aiUsage;
	cost = { totalCost: result.aiUsage?.cost };
	break;
	case 'agent_performance':
	result = await executeAgentPerformance(params, env);
	break;
	case 'general_chat':
	result = await executeGeneralChat(params, env);
	usage = result.usage;
	cost = result.cost;
	break;
	default:
	throw new Error(`Unknown intent: ${intent}`);
	}
	console.log(`[${requestId}] ✅ Query executed successfully`);
	const response: QueryResponse = {
	query: body.query,
	intent,
	result,
	usage,
	cost,
	timestamp: new Date().toISOString(),
	};
	return new Response(JSON.stringify(response, null, 2), {
	status: 200,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	} catch (error) {
	console.error(`[${requestId}] ❌ Query error:`, error);
	return new Response(JSON.stringify({
	error: error instanceof Error ? error.message : 'Unknown error',
	timestamp: new Date().toISOString(),
	}), {
	status: 500,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	}
	import { z } from 'zod';
	const QueryRequestSchema = z.object({
	query: z.string().min(1).max(1000),
	agentID: z.string().optional(),
	});
	export async function handleAIQuery(request: Request, env: Env): Promise<Response> {
	const requestId = Date.now().toString(36);
	console.log(`[${requestId}] 🔍 AI query request received`);
	try {
	// Validate API key
	if (!env.KIMI_API_KEY || env.KIMI_API_KEY === 'sk-placeholder-use-dotenv-or-secrets') {
	return new Response(JSON.stringify({
	error: 'AI service not configured',
	requestId,
	timestamp: new Date().toISOString(),
	}), {
	status: 503,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	// Parse & validate request body
	let body: QueryRequest;
	try {
	const rawBody = await request.json();
	body = QueryRequestSchema.parse(rawBody);
	} catch (error) {
	return new Response(JSON.stringify({
	error: error instanceof Error ? error.message : 'Invalid request body',
	requestId,
	timestamp: new Date().toISOString(),
	}), {
	status: 400,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	console.log(`[${requestId}] Query: "${body.query}"`);
	// Parse intent
	const { intent, params } = parseQuery(body.query);
	console.log(`[${requestId}] Intent: ${intent}`, params);
	// Add agentID if provided
	if (body.agentID) {
	params.agentID = body.agentID;
	}
	// Execute based on intent
	let result: any;
	let usage: any;
	let cost: any;
	switch (intent) {
	case 'sharp_analysis':
	result = await executeSharpAnalysis(params, env);
	usage = result.aiUsage;
	cost = { totalCost: result.aiUsage?.cost };
	break;
	case 'steam_detection':
	result = await executeSteamDetection(params, env);
	usage = result.aiUsage;
	cost = { totalCost: result.aiUsage?.cost };
	break;
	case 'risk_report':
	result = await executeRiskReport(params, env);
	usage = result.aiUsage;
	cost = { totalCost: result.aiUsage?.cost };
	break;
	case 'agent_performance':
	result = await executeAgentPerformance(params, env);
	break;
	case 'general_chat':
	result = await executeGeneralChat(params, env);
	usage = result.usage;
	cost = result.cost;
	break;
	default:
	throw new Error(`Unknown intent: ${intent}`);
	}
	console.log(`[${requestId}] ✅ Query executed successfully`);
	const response: QueryResponse = {
	query: body.query,
	intent,
	result,
	usage,
	cost,
	requestId,
	timestamp: new Date().toISOString(),
	};
	return new Response(JSON.stringify(response, null, 2), {
	status: 200,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	} catch (error) {
	console.error(`[${requestId}] ❌ Query error:`, error);
	return new Response(JSON.stringify({
	error: error instanceof Error ? error.message : 'Unknown error',
	requestId,
	timestamp: new Date().toISOString(),
	}), {
	status: 500,
	headers: { ...CORS_HEADERS, 'Content-Type': 'application/json' },
	});
	}
	}

🤖 Prompt for AI Agents

In src/api/ai-query.ts around lines 207-305, the handler must be hardened and
follow project guidelines: wrap the exported handler with the asyncHandler(...)
wrapper and remove any uncaught async boundaries; validate the incoming body
with a Zod schema (define a QueryRequest zod schema and use safeParse on the
parsed JSON) and handle malformed JSON by catching request.json() errors (or
perform JSON.parse in a try/catch before zod), and return a 400 with a
structured error when validation fails; include requestId in both the success
response payload and the error response payload (add requestId to the
QueryResponse interface and ensure createErrorResponse / the error response
include it); and ensure all code paths that set usage/cost produce defined
values (initialize usage and cost to null/defaults). Make minimal edits: add Zod
schema + safeParse, wrap handler export with asyncHandler, add try/catch around
await request.json() (or catch parse errors before zod), include requestId and
timestamp in both success and error JSON responses, and update the QueryResponse
type to include requestId.