Orchestrator MCP

Instructions

Implementation Reference

• src/server/handlers/orchestrator.ts:62-92 (handler)

  The primary handler function for the 'ai_process' MCP tool. It extracts the natural language request from input arguments, validates it, delegates processing to the AIOrchestrator, and returns the AI-generated response as MCP content.
  export async function handleAIProcess(args: any, aiOrchestrator: AIOrchestrator) { try { const request = args.request; if (!request) { throw new Error('Request parameter is required'); } logger.info(`Processing AI request: ${request.substring(0, 100)}...`); const result = await aiOrchestrator.processRequest(request); return { content: [ { type: 'text', text: result.response, }, ], }; } catch (error) { logger.error('Failed to process AI request:', error as Error); return { content: [ { type: 'text', text: `Error processing request: ${error instanceof Error ? error.message : String(error)}`, }, ], }; } }
• src/server/handlers/dispatcher.ts:37-39 (registration)

  Registration of the 'ai_process' tool in the main tool call dispatcher switch statement, routing calls to the handleAIProcess function.
  case 'ai_process': return handleAIProcess(args, aiOrchestrator);
• src/server/handlers/tools.ts:25-36 (schema)

  JSON Schema definition for the 'ai_process' tool input, specifying a required 'request' string parameter with descriptive examples.
  inputSchema: { type: 'object', properties: { request: { type: 'string', description: 'Natural language description of what you want to accomplish. Examples: "Search for React 19 features and analyze code examples", "Find all TypeScript files with TODO comments", "Check git status and create a summary of recent changes", "Fetch the latest Next.js documentation and extract routing information"', }, }, required: ['request'], additionalProperties: false, $schema: 'http://json-schema.org/draft-07/schema#', },
• src/server/handlers/tools.ts:22-37 (registration)

  Tool definition object for 'ai_process' returned by the listTools handler, including name, description, and input schema.
  { name: 'ai_process', description: 'Primary AI orchestration interface - intelligently processes complex requests by automatically selecting and coordinating multiple tools. Handles file operations, git management, web search, web fetching, browser automation, security analysis, and more. Describe your goal naturally - the AI will determine the best approach and execute multi-step workflows.', inputSchema: { type: 'object', properties: { request: { type: 'string', description: 'Natural language description of what you want to accomplish. Examples: "Search for React 19 features and analyze code examples", "Find all TypeScript files with TODO comments", "Check git status and create a summary of recent changes", "Fetch the latest Next.js documentation and extract routing information"', }, }, required: ['request'], additionalProperties: false, $schema: 'http://json-schema.org/draft-07/schema#', }, },
• src/ai/orchestrator.ts:72-122 (helper)

  Core processing method in AIOrchestrator class called by the tool handler. Orchestrates the AI workflow execution and formats the result with metadata.
  async processRequest(userRequest: string): Promise<AIOrchestrationResult> { if (!this.initialized) { throw new Error('AI Orchestrator not initialized. Call initialize() first.'); } const startTime = Date.now(); console.error(`🤖 AI Processing: "${userRequest}"`); try { // Execute AI-driven workflow const workflowResult = await this.workflowEngine.executeWorkflow(userRequest); const processingTime = Date.now() - startTime; const toolsUsed = workflowResult.context.results.map(r => r.tool); const successfulSteps = workflowResult.context.results.filter(r => r.success).length; console.error(`🎯 AI Processing completed in ${processingTime}ms`); return { success: workflowResult.success, response: workflowResult.finalResult, metadata: { processingTime, toolsUsed, confidence: this.calculateOverallConfidence(workflowResult.context.steps), workflowSteps: workflowResult.context.results.length, aiEnhanced: true, }, rawResults: workflowResult.context.results.map(r => r.result), }; } catch (error) { const processingTime = Date.now() - startTime; const errorMessage = error instanceof Error ? error.message : String(error); console.error(`💥 AI Processing failed: ${errorMessage}`); return { success: false, response: `I encountered an error while processing your request: ${errorMessage}`, metadata: { processingTime, toolsUsed: [], confidence: 0, workflowSteps: 0, aiEnhanced: true, }, error: errorMessage, }; } }