MCP server that exposes Gemini web search and document question answering tools.
Project description
Gemini Search MCP
Gemini Search MCP packages a Model Context Protocol server that exposes five tools:
- web_search – Uses Gemini with Google Search grounding to answer general questions.
- document_question_answering – Converts local documents to captioned markdown and asks Gemini to answer questions about their contents.
- get_document_content – Converts a document to markdown and returns the full content for reading.
- get_document_chunk – Retrieves specific chunks of large documents for easier processing.
- get_next_chunk – Automatically continues reading from where you left off (stateful).
Installation
Python (pip)
pip install gemini-search-mcp
Node.js (npm)
npm install -g gemini-search-mcp
Usage
Set your Google API key (must have Gemini access):
export GOOGLE_API_KEY="your-key"
Run the MCP server (defaults to stdio transport):
gemini-search-mcp run
# or simply
# gemini-search-mcp
Configure Codex automatically (writes to ~/.codex/config.toml by default):
gemini-search-mcp configure --api-key "YOUR_KEY"
Configure Copilot CLI (writes to ~/.copilot/config.json):
gemini-search-mcp configure --cli-type copilot --api-key "YOUR_KEY"
Configure both Codex and Copilot CLI at once:
gemini-search-mcp configure --cli-type both --api-key "YOUR_KEY"
For npm/npx installation with custom command:
gemini-search-mcp configure --command npx --command-args -y gemini-search-mcp --api-key "YOUR_KEY"
Clear cached conversion artifacts:
gemini-search-mcp clear-cache
# 선택 옵션: --cache-dir /custom/path --remove-root
Development
Install in editable mode with testing dependencies:
pip install -e .
Ensure LibreOffice is installed and on PATH if you plan to convert non-PDF documents.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Publishing
For maintainers: See PUBLISHING.md for instructions on how to publish new versions to PyPI and npm.
Changelog
See CHANGELOG.md for a list of changes in each version.
License
MIT – all rights reserved.
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