Gemini DeepSearch MCP
Leverages Google Search to perform deep web research, generating queries and synthesizing information from search results
Uses Google Gemini models (Flash and Pro) to power automated research capabilities, with configurable effort levels for research depth
Utilizes LangGraph for workflow management and state tracking during the multi-step research process
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Gemini DeepSearch MCPresearch the latest quantum computing breakthroughs with high effort"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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: Deep Research MCP Server
Usage
Development Server (HTTP + Studio UI)
Start the LangGraph development server with Studio UI:
make devLocal MCP Server (stdio)
Start the MCP server with stdio transport for integration with MCP clients:
make localTesting
Run the test suite:
make testTest the MCP stdio server:
make test_mcpUse MCP inspector
make inspectWith Langsmith tracing
GEMINI_API_KEY=AI******* LANGSMITH_API_KEY=ls******* LANGSMITH_TRACING=true make inspectAPI
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_KEYenvironment variable
Installation
Install directly using uvx:
uvx install gemini-deepsearch-mcpClaude 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-herewith your actual Gemini API keyRestart 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
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
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