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Gemini DeepSearch MCP

by alexcong
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

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

Related MCP server: MCP Hub

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

Available Tools

1 tool

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • Changeddeep_search3 fields changed
      • removedInput schema / properties / effort / title
        Removed value: -"Effort"
      • removedInput schema / properties / query / title
        Removed value: -"Query"
      • changedOutput schema / (root)
        Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
  2. 1 tool update
    • First observeddeep_search
Install Server
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TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'deep_search' has a clear, distinct purpose focused on performing deep web research.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'deep_search' follows a clear verb_noun pattern and is descriptive of its function.

Tool Count2/5

A single tool is generally too few for most server purposes, as it limits functionality and flexibility. While it might suffice for a very narrow scope, it feels thin and incomplete for a server named 'Gemini DeepSearch MCP', which suggests broader research capabilities.

Completeness2/5

The server is severely incomplete for a deep search/research domain. There are no tools for refining searches, managing search history, filtering results, or handling different types of queries. The single tool provides a basic search function but lacks the comprehensive coverage expected for a research-oriented server.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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