ThinkingCap

Implementation Reference

• src/index.ts:42-84 (registration)

  Registration of the "research" MCP tool, including schema and handler function that orchestrates multi-agent research.
  server.tool( "research", { query: z.string().describe("The research query or question to investigate"), maxSearchResults: z .number() .min(1) .max(20) .default(5) .describe("Maximum web search results per agent"), }, async ({ query, maxSearchResults }) => { try { const result = await orchestrate(query, { agents: agentSpecs, temperature: 0.7, maxSearchResults: maxSearchResults ?? 5, maxIterations: 10, }); return { content: [ { type: "text" as const, text: result, }, ], }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [ { type: "text" as const, text: `Research failed: ${errorMessage}`, }, ], isError: true, }; } } );
• src/orchestrator.ts:207-252 (handler)

  Core handler logic for the research tool: orchestrates multiple LLM agents, generates sub-questions, performs web searches, and synthesizes final response.
  export async function orchestrate( query: string, config: OrchestratorConfig ): Promise<string> { const { agents, temperature, maxSearchResults } = config; if (agents.length === 0) { throw new Error("At least one agent must be specified"); } // Single agent mode - just run directly if (agents.length === 1) { const result = await runAgent( 0, agents[0], query, temperature, maxSearchResults ); if (result.status === "error") { throw new Error(result.error); } return result.response; } // Multi-agent mode // Generate questions using the first agent const questions = await generateQuestions( query, agents.length, agents[0], temperature ); // Run all agents in parallel const agentPromises = agents.map((agentSpec, i) => runAgent(i, agentSpec, questions[i], temperature, maxSearchResults) ); const results = await Promise.all(agentPromises); // Synthesize responses using the first agent const finalResponse = await synthesizeResponses(results, agents[0], temperature); return finalResponse; }
• src/orchestrator.ts:108-153 (helper)

  Helper function that runs a single agent: performs web search, calls LLM with system prompt and search results.
  async function runAgent( agentId: number, agentSpec: string, question: string, temperature: number, maxSearchResults: number ): Promise<AgentResult> { const { provider, model } = parseAgentSpec(agentSpec); try { // Perform web search const searchResults = await searchWeb(question, maxSearchResults); const formattedResults = formatSearchResults(searchResults); // Build messages const messages: Message[] = [ { role: "system", content: SYSTEM_PROMPT }, { role: "user", content: `Question: ${question}\n\nSearch Results:\n${formattedResults}\n\nPlease provide a comprehensive answer based on the search results above.`, }, ]; // Call LLM const response = await callLLM(provider, model, messages, temperature); return { agentId, provider, model, question, response: response.content, status: "success", }; } catch (error) { return { agentId, provider, model, question, response: "", status: "error", error: error instanceof Error ? error.message : String(error), }; } }
• src/orchestrator.ts:63-103 (helper)

  Helper to generate diverse sub-questions for multi-agent research using an LLM.
  async function generateQuestions( query: string, numAgents: number, agentSpec: string, temperature: number ): Promise<string[]> { const { provider, model } = parseAgentSpec(agentSpec); const prompt = QUESTION_GENERATION_PROMPT.replace("{query}", query).replace( "{numAgents}", numAgents.toString() ); try { const response = await callLLM( provider, model, [{ role: "user", content: prompt }], temperature ); // Parse JSON response const jsonMatch = response.content.match(/\[[\s\S]*\]/); if (jsonMatch) { const questions = JSON.parse(jsonMatch[0]) as string[]; if (questions.length === numAgents) { return questions; } } } catch (error) { console.error("Question generation failed:", error); } // Fallback questions return [ `Research comprehensive information about: ${query}`, `Analyze and provide insights about: ${query}`, `Find alternative perspectives on: ${query}`, `Verify and cross-check facts about: ${query}`, ].slice(0, numAgents); }
• src/index.ts:44-52 (schema)

  Input schema for the research tool using Zod validation.
  { query: z.string().describe("The research query or question to investigate"), maxSearchResults: z .number() .min(1) .max(20) .default(5) .describe("Maximum web search results per agent"), },