Ultra MCP

Instructions

Implementation Reference

• src/handlers/ai-tools.ts:222-263 (handler)

  Core handler function that executes the 'research' tool. Selects AI provider, constructs research-specific system prompt based on output format and sources, generates response via provider.generateText, and returns structured content with metadata.
  async handleResearch(params: z.infer<typeof ResearchSchema>) { // Use provided provider or get the preferred one (Azure if configured) const providerName = params.provider || (await this.providerManager.getPreferredProvider(['openai', 'gemini', 'azure', 'grok'])); const provider = await this.providerManager.getProvider(providerName); // Build research prompts based on output format const formatInstructions = { summary: "Provide a concise summary of key findings and insights.", detailed: "Provide a comprehensive analysis with detailed findings, evidence, and conclusions.", academic: "Present findings in an academic format with clear structure, citations where possible, and scholarly analysis.", }; const systemPrompt = `You are an expert researcher with deep knowledge across multiple domains. Your task is to conduct thorough research, analyze information critically, and present findings clearly. ${params.sources ? `Consider these specific sources or contexts: ${params.sources.join(", ")}` : ""} ${formatInstructions[params.outputFormat]}`; const response = await provider.generateText({ prompt: `Research the following: ${params.query}`, model: params.model, systemPrompt, reasoningEffort: (providerName === "openai" || providerName === "azure" || providerName === "grok") ? "high" : undefined, useSearchGrounding: providerName === "gemini", // Always enable search for research with Gemini temperature: 0.4, // Lower temperature for research accuracy }); return { content: [ { type: "text", text: response.text, }, ], metadata: { provider: providerName, model: response.model, outputFormat: params.outputFormat, usage: response.usage, ...response.metadata, }, }; }
• src/server.ts:264-272 (registration)

  Registers the 'research' tool with MCP server, defining title, description, input schema, and handler delegation to AIToolHandlers.handleResearch.
  // Register research tool server.registerTool("research", { title: "Research", description: "Conduct comprehensive research with multiple output formats", inputSchema: ResearchSchema.shape, }, async (args) => { const aiHandlers = await getHandlers(); return await aiHandlers.handleResearch(args); });
• src/server.ts:26-32 (schema)

  Zod schema defining input parameters for the 'research' tool: provider, query (required), sources, model, outputFormat.
  const ResearchSchema = z.object({ provider: z.enum(["openai", "gemini", "azure", "grok"]).optional().describe("AI provider to use (defaults to Azure if configured, otherwise best available)"), query: z.string().describe("Research query or topic"), sources: z.array(z.string()).optional().describe("Specific sources or contexts to consider"), model: z.string().optional().describe("Specific model to use"), outputFormat: z.enum(["summary", "detailed", "academic"]).default("detailed").describe("Output format for research"), });
• src/handlers/ai-tools.ts:26-32 (schema)

  Zod schema for 'research' tool inputs, used in handler type inference (identical to server.ts definition).
  const ResearchSchema = z.object({ provider: z.enum(["openai", "gemini", "azure", "grok"]).optional().describe("AI provider to use (defaults to Azure if configured, otherwise best available)"), query: z.string().describe("Research query or topic"), sources: z.array(z.string()).optional().describe("Specific sources or contexts to consider"), model: z.string().optional().describe("Specific model to use"), outputFormat: z.enum(["summary", "detailed", "academic"]).default("detailed").describe("Output format for research"), });
• src/server.ts:551-569 (registration)

  Registers a prompt template for natural language invocation of the 'research' tool, mapping args to a user message prompt.
  server.registerPrompt("research", { title: "Comprehensive Research", description: "Conduct thorough research on any topic with multiple output formats", argsSchema: { query: z.string().optional(), provider: z.string().optional(), model: z.string().optional(), outputFormat: z.string().optional(), sources: z.string().optional(), }, }, (args) => ({ messages: [{ role: "user", content: { type: "text", text: `Research this topic thoroughly: ${args.query || 'Please specify a research topic or query.'}${args.outputFormat ? ` (format: ${args.outputFormat})` : ''}${args.sources ? `\n\nFocus on these sources: ${args.sources}` : ''}${args.provider ? ` (using ${args.provider} provider)` : ''}` } }] }));