Gemini DeepSearch MCP

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.

Features

• 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

Usage

Development Server (HTTP + Studio UI)

Start the LangGraph development server with Studio UI:

make dev

Local MCP Server (stdio)

Start the MCP server with stdio transport for integration with MCP clients:

make local

Testing

Run the test suite:

make test

Test the MCP stdio server:

make test_mcp

Use MCP inspector

make inspect

With Langsmith tracing

GEMINI_API_KEY=AI******* LANGSMITH_API_KEY=ls******* LANGSMITH_TRACING=true make inspect

API

The `deep_search` tool accepts:

• query (string): The research question or topic to investigate
• effort (string): Research effort level - "low", "medium", or "high"
  • Low: 1 query, 1 loop, Flash model
  • Medium: 3 queries, 2 loops, Flash model
  • High: 5 queries, 3 loops, Pro model

Return Format

HTTP MCP Server (Development mode):

• answer: Comprehensive research response with citations
• sources: List of source URLs used in research

Stdio MCP Server (Claude Desktop integration):

• file_path: Path to a JSON file containing the research results

The stdio MCP server writes results to a JSON file in the system temp directory to optimize token usage. The JSON file contains the same `answer` and `sources` data as the HTTP version, but is accessed via file path rather than returned directly.

Requirements

• Python 3.12+
• `GEMINI_API_KEY` environment variable

Installation

Install directly using uvx:

uvx install gemini-deepsearch-mcp

Claude Desktop Integration

To use the MCP server with Claude Desktop, add this configuration to your Claude Desktop config file:

macOS

Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:

{
  "mcpServers": {
    "gemini-deepsearch": {
      "command": "uvx",
      "args": ["gemini-deepsearch-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      },
      "timeout": 180000
    }
  }
}

Windows

Edit `%APPDATA%/Claude/claude_desktop_config.json`:

{
  "mcpServers": {
    "gemini-deepsearch": {
      "command": "uvx",
      "args": ["gemini-deepsearch-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      },
      "timeout": 180000
    }
  }
}

Linux

Edit `~/.config/claude/claude_desktop_config.json`:

{
  "mcpServers": {
    "gemini-deepsearch": {
      "command": "uvx",
      "args": ["gemini-deepsearch-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      },
      "timeout": 180000
    }
  }
}

Important:

• Replace `your-gemini-api-key-here` with your actual Gemini API key
• Restart Claude Desktop after updating the configuration
• Set ample timeout to avoid `MCP error -32001: Request timed out`

Alternative: Local Development Setup

For development or if you prefer to run from source:

{
  "mcpServers": {
    "gemini-deepsearch": {
      "command": "uv",
      "args": ["run", "python", "main.py"],
      "cwd": "/path/to/gemini-deepsearch-mcp",
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Replace `/path/to/gemini-deepsearch-mcp` with the actual absolute path to your project directory.

Once configured, you can use the `deep_search` tool in Claude Desktop by asking questions like:

• "Use deep_search to research the latest developments in quantum computing"
• "Search for information about renewable energy trends with high effort"

Agent Source

The deep search agent is from the Gemini Fullstack LangGraph Quickstart repository.

License

MIT