Kagi MCP server

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:

• 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:

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Development/Unpublished Servers Configuration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:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

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

npx @modelcontextprotocol/inspector uv --directory path_to_project run kagi-mcp