Google Researcher MCP Server

@zoharbabin

Basic information

About Google Researcher MCP Server

Power your AI agents with Google Search–enhanced research via an open-source MCP server. Includes tools for Google Search, YouTube/web scraping, LLM-driven synthesis, persistent caching, and dual transport (STDIO + HTTP SSE) for efficient, flexible integration.

Google Researcher MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

Google Researcher MCP Server tools

Search the web

Extract content from URLs

Process text with AI

Combined research workflow

Overview

What is Google Researcher MCP Server?

Google Researcher MCP Server gives AI assistants real-time web research abilities via Google Search, content scraping, and Gemini AI analysis. It implements the Model Context Protocol (MCP) and uses persistent caching to reduce API costs and improve performance. The server is built for developers integrating research capabilities into MCP‑compatible AI clients.

How to use Google Researcher MCP Server?

Clone the repository, install dependencies, copy .env.example to .env, and fill in your Google Custom Search API key, Custom Search Engine ID, and Gemini API key. Run in development mode with npm run dev or build and run production with npm run build && npm start. The server provides four tools – google_search, scrape_page, analyze_with_gemini, and research_topic – callable over STDIO or HTTP+SSE transports.

Key features of Google Researcher MCP Server

Four research tools: search, scrape, analyze, and combined workflow
Persistent caching (memory + disk) for repeated queries
Session resumption support for web clients
Dual transport: STDIO and HTTP+SSE
Management API endpoints for cache and event store statistics
OAuth 2.1 authorization with granular scope control

Use cases of Google Researcher MCP Server

Enable an AI coding assistant to look up current web information during development
Automate research workflows that collect, scrape, and summarize multiple sources
Build a conversational agent that answers questions using real‑time search results
Reduce API costs by caching identical search and analysis requests

Frequently asked questions

What runtime and API keys does the server require?

Node.js v18 or later, plus API keys for Google Custom Search, a Google Custom Search Engine ID, and a Google Gemini API key.

How do I run the server for development?

Use `npm run dev` for automatic reloading on file changes, or build and start with `npm run build && npm start` for production.

What transport options are available?

The server supports STDIO (direct process) and HTTP+SSE (web) transports. HTTP+SSE requires OAuth 2.1 authentication with a Bearer token.

Can I monitor or clear the cache?

Yes. HTTP management endpoints include `GET /mcp/cache-stats`, `POST /mcp/cache-invalidate`, and `POST /mcp/cache-persist`, each protected by appropriate OAuth scopes.

How do I integrate this server with Roo Code?

Create a `.roo/mcp.json` file in your project with the server’s path and environment variables, using STDIO transport. The README provides a complete example configuration.

Comments

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