gemini-deepsearch-mcp

# 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, …