https://github.com/alexcong/gemini-deepsearch-mcp
https://github.com/alexcong/gemini-deepsearch-mcp
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/alexcong/gemini-deepsearch-mcp
- Owner: alexcong
- Created: 2025-06-04T02:34:50.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-06-22T00:14:50.000Z (3 months ago)
- Last Synced: 2025-06-22T00:46:21.876Z (3 months ago)
- Language: Python
- Size: 89.8 KB
- Stars: 19
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```bash
make dev
```
### Local MCP Server (stdio)
Start the MCP server with stdio transport for integration with MCP clients:
```bash
make local
```
### Testing
Run the test suite:
```bash
make test
```
Test the MCP stdio server:
```bash
make test_mcp
```
Use MCP inspector
```bash
make inspect
```
With Langsmith tracing
```bash
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:
```bash
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`:
```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`:
```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`:
```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:
```json
{
"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](https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart) repository.
## License
MIT