Gemini Deepsearch MCP Server

Gemini Deepsearch MCP becomes available to MCP clients through the gemini deepsearch mcp mcp server. Gemini DeepSearch MCP is an automated research agent that leverages Google Gemini models and Google Search to perform deep, multi-step web research. It generates sophisticated queries, synthesizes information from search results, identifies knowledge gaps, and produces high-quality, citation-rich.

What Gemini Deepsearch MCP does

Gemini DeepSearch MCP is an automated research agent that leverages Google Gemini models and Google Search to perform deep, multi-step web research. It generates sophisticated queries, synthesizes information from search results, identifies knowledge gaps, and produces high-quality, citation-rich answers.

Key capabilities

• Automated multi-step research — using Gemini models and Google Search
• FastMCP integration — for both HTTP API and stdio deployment
• Configurable effort levels — (low, medium, high) for research depth
• Citation-rich responses — with source tracking
• LangGraph-powered workflow — with state management

Tools it exposes

Once connected, the assistant can call these 7 tools directly:

• Low — 1 query, 1 loop, Flash model
• Medium — 3 queries, 2 loops, Flash model
• High — 5 queries, 3 loops, Pro model
• Testing — With Langsmith tracing bash GEMINI_API_KEY=AI******* LANGSMITH_API_KEY=ls******* LANGSMITH_TRACING=true make inspect
• macOS — Edit ~/Library/Application Support/Claude/claude_desktop_config.json:
• Windows — The Windows tool exposed by this server
• Linux — The Linux tool exposed by this server

Installing the gemini deepsearch mcp mcp server

The server is distributed via PyPI as install, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.

Configuration

Before the server will start you need to supply 2 environment variables: GEMINI_API_KEY, LANGSMITH_API_KEY. Keep credentials in your client's env block or a secrets manager rather than committing them.

Requirements

• Python 3.12+ - GEMINI_API_KEY environment variable

Where it fits

Developer-tool servers are usually the first ones people connect, because they turn "help me with this code" into an assistant that can actually read the repo and act on it. Gemini Deepsearch MCP sits in that group, and the shape of its toolset — Low, Medium, High among others — tells you what it is really for. Worth comparing against the other developer tools servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.

Practical notes

• This server runs locally, so it operates with whatever access your machine and its credentials already have. Scope that deliberately rather than by default.
• It will not start until its required credentials are present, so set those before wondering why the tools never appear.
• With 7 tools exposed, expect a noticeable bump in prompt size — disable it in projects that never use Gemini Deepsearch MCP.
• Maintained by alexcong, written in Python.
• MCP clients ask for confirmation before each tool call by default. Keep that on while you learn what the gemini deepsearch mcp mcp server actually does with your data.
• Every entry in this directory is reviewed by hand before it goes live, and details are checked against the project's own documentation.