Kagi MCP Server

# Kagi MCP server [](https://smithery.ai/client/kagi-mcp) MCP server that allows to search web using Kagi API <a href="https://glama.ai/mcp/servers/rl6yu8g58l"><img width="380" height="200" src="https://glama.ai/mcp/servers/rl6yu8g58l/badge" alt="Kagi Server MCP server" /></a> ## Components ### Resources The server implements calls of [API methods](https://help.kagi.com/kagi/api/overview.html): - fastgpt - enrich/web - enrich/news ### Prompts The server provides doesn't provide any prompts: ### Tools The server implements several tools: - ask_fastgpt to search web and find an answer - enrich_web to enrich model context with web content - enrich_news to enrich model context with latest news ## Configuration ## Quickstart ### Install ### Installing via Smithery To install the Kagi MCP server for Claude Desktop automatically via [Smithery](https://smithery.ai/client/kagi-mcp): ```bash npx -y @smithery/cli install kagi-mcp --client claude ``` #### Claude Desktop On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` <details> <summary>Development/Unpublished Servers Configuration</summary> ``` "mcpServers": { "kagi-mcp": { "command": "uv", "args": [ "--directory", "path_to_project", "run", "kagi-mcp" ], "env": { "KAGI_API_KEY": "YOUR API KEY" } } } ``` </details> ## Development ### Building and Publishing To prepare the package for distribution: 1. Sync dependencies and update lockfile: ```bash uv sync ``` 2. Build package distributions: ```bash uv build ``` This will create source and wheel distributions in the `dist/` directory. 3. Publish to PyPI: ```bash uv publish ``` Note: You'll need to set PyPI credentials via environment variables or command flags: - Token: `--token` or `UV_PUBLISH_TOKEN` - Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD` ### Debugging ```bash npx @modelcontextprotocol/inspector uv --directory path_to_project run kagi-mcp ```