Perplexity Agent MCP

Instructions

Implementation Reference

• src/tools/answer.ts:25-35 (handler)

  The handler function for the 'answer' tool. It takes a question, performs a chat completion using Perplexity's sonar-reasoning-pro model with the ANSWER_SYSTEM_PROMPT, and returns the result as text content.
  async ({ question }) => { const result = await performChatCompletion( [{ role: "user", content: question }], { model: "sonar-reasoning-pro", system: ANSWER_SYSTEM_PROMPT, searchContextSize: "high", }, ); return { content: [{ type: "text", text: result }] }; },
• src/tools/answer.ts:13-24 (schema)

  Tool schema definition including description and inputSchema with 'question' parameter using Zod validation.
  { description: ` Researches a question, compares options, and recommends a path (backed by sources). Use for library choices, architecture trade-offs, migrations, complex debugging, and performance decisions. Returns a concise recommendation, a brief why, and short how-to steps. Examples: "Should I use Zod or Valibot?", "How to optimize React bundle size?", "Best auth approach for Node.js microservices?" One question per call—split combined requests into separate queries. `.trim(), inputSchema: { question: z.string().describe("The decision or problem to answer"), }, },
• src/tools/answer.ts:11-36 (registration)

  The server.registerTool call within registerAnswerTool that performs the actual MCP tool registration for 'answer'.
  server.registerTool( "answer", { description: ` Researches a question, compares options, and recommends a path (backed by sources). Use for library choices, architecture trade-offs, migrations, complex debugging, and performance decisions. Returns a concise recommendation, a brief why, and short how-to steps. Examples: "Should I use Zod or Valibot?", "How to optimize React bundle size?", "Best auth approach for Node.js microservices?" One question per call—split combined requests into separate queries. `.trim(), inputSchema: { question: z.string().describe("The decision or problem to answer"), }, }, async ({ question }) => { const result = await performChatCompletion( [{ role: "user", content: question }], { model: "sonar-reasoning-pro", system: ANSWER_SYSTEM_PROMPT, searchContextSize: "high", }, ); return { content: [{ type: "text", text: result }] }; }, );
• src/server.ts:17-18 (registration)

  Invocation of registerAnswerTool during MCP server creation to register the 'answer' tool.
  registerLookupTool(server); registerAnswerTool(server);
• src/prompts.ts:73-102 (helper)

  The system prompt used in the 'answer' tool handler for guiding the AI in technical decision making.
  export const ANSWER_SYSTEM_PROMPT = ` # Role: Technical Decision & Analysis Agent Research complex questions, compare approaches, and provide actionable recommendations. Optimized for: - Architecture decisions and design patterns - Library/framework selection and migration paths - Performance optimization strategies - Debugging complex issues across systems - Best practices and trade-off analysis # Instructions - Start with a brief analysis plan (3-5 conceptual steps) to structure your research - Search multiple sources to compare different approaches - Analyze real-world usage patterns in popular repositories - Weigh trade-offs based on the user's specific constraints - Provide a decisive recommendation with clear justification # Output Structure - **Recommendation:** Your advised approach in 1-2 sentences - **Why:** Key reasons with evidence from source code or benchmarks - **Implementation:** Practical steps with working code example - **Trade-offs:** What you gain vs what you sacrifice - **Alternatives:** Other viable options if constraints change ${AUTHORITATIVE_SOURCES} # Guidance - Use modern ESM and TypeScript for examples by default, but adapt language and examples as appropriate to the question. - Be decisive in your conclusions, but transparent about any uncertainty. - Present only your final conclusions and justification—avoid extraneous commentary or process narration. `.trim();