Kagi MCP Server

# kagi-ken-mcp Node.js MCP server providing Kagi search, summarization and AI assistant tools using your existing Kagi session token. ## Features - **Search**: Kagi web search with multiple query support - **Summarizer**: URL/content summarization with customizable formats - **Assistant**: AI-powered conversations using Kagi's AI models ## Environment Variables The server requires different environment variables depending on which features you want to use: ### For Search and Summarization only: - `KAGI_SESSION_TOKEN`: Your Kagi session token - `KAGI_SUMMARIZER_ENGINE`: Summarizer engine to use (optional, default: "default") ### For Assistant feature (in addition to the above): - `KAGI_SEARCH_COOKIE`: Your `_kagi_search_` cookie value - `KAGI_MODEL_LIST`: Comma-separated list of available AI models (required for assistant) - `KAGI_DEFAULT_MODEL`: Default model to use (optional, uses first from list if not specified) ## Setup ### Get Required Tokens #### Session Token 1. Visit [Kagi Settings](https://kagi.com/settings/user_details) 2. Copy the **Session Link** 3. Extract the `token` value 4. Set `KAGI_SESSION_TOKEN` env variable #### Search Cookie (for Assistant feature) 1. Open browser developer tools (F12) 2. Go to Kagi.com and login 3. In Application/Storage tab, find Cookies for kagi.com 4. Copy the value of `_kagi_search_` cookie 5. Set `KAGI_SEARCH_COOKIE` env variable #### Model Configuration (for Assistant feature) - Set `KAGI_MODEL_LIST` with comma-separated available models (e.g., "o3-pro,claude-4-sonnet,gemini-2-5-pro") - Optionally set `KAGI_DEFAULT_MODEL` to specify default model ### Claude Desktop Add to `claude_desktop_config.json`: ```json { "mcpServers": { "kagi-mcp": { "command": "npx", "args": ["-y", "@duange/kagi-mcp"], "env": { "KAGI_SESSION_TOKEN": "YOUR_SESSION_TOKEN_HERE", "KAGI_SEARCH_COOKIE": "YOUR_KAGI_SEARCH_COOKIE_HERE", "KAGI_MODEL_LIST": "o3-pro,claude-4-sonnet,gemini-2-5-pro", "KAGI_DEFAULT_MODEL": "claude-4-sonnet", "KAGI_SUMMARIZER_ENGINE": "default" } } } } ``` #### Post-install [Disable Claude Desktop's built-in websearch](assets/claude-desktop-disable-websearch.png) so it'll use this here MCP server. And maybe add this to your "Personal preferences" (i.e., system prompt) in Settings: ``` For web searches, use kagi-ken-mcp MCP server's `kagi_search_fetch` tool. For summarizing a URL, use the kagi-ken-mcp MCP server's `kagi_summarizer` tool. For AI conversations, use the kagi-ken-mcp MCP server's `kagi_assistant` tool. ``` ### Claude Code Add MCP server to Claude Code: ```bash claude mcp add kagi-mcp \ --scope user \ --env KAGI_SESSION_TOKEN="YOUR_SESSION_TOKEN_HERE" \ --env KAGI_SEARCH_COOKIE="YOUR_KAGI_SEARCH_COOKIE_HERE" \ --env KAGI_MODEL_LIST="o3-pro,claude-4-sonnet,gemini-2-5-pro" \ --env KAGI_DEFAULT_MODEL="claude-4-sonnet" \ npx -y @duange/kagi-mcp ``` #### Post-install Disable Claude Code's built-in web search (optional) by setting the permission in the relevant `.claude/settings*.json` file: ```json { "permissions": { "deny": [ "WebSearch" ], "allow": [ "mcp__kagi-mcp__kagi_search_fetch", "mcp__kagi-mcp__kagi_summarizer", "mcp__kagi-mcp__kagi_assistant" ] } } ``` ## Usage: Pose query that requires use of a tool e.g. _"Who was time's 2024 person of the year?"_ for search, or "summarize this video: https://www.youtube.com/watch?v=sczwaYyaevY" for summarizer. ## Tools ### `kagi_search_fetch` Fetch web results based on one or more queries using the Kagi Search API. Results are numbered continuously for easy reference. **Parameters:** - `queries` (array of strings): One or more search queries ### `kagi_summarizer` Summarize content from URLs using the Kagi Summarizer API. Supports various document types including webpages, videos, and audio. **Parameters:** - `url` (string): URL to summarize - `summary_type` (enum): `"summary"` for paragraph prose or `"takeaway"` for bullet points (default: `"summary"`) - `target_language` (string, optional): Language code (e.g., `"EN"` for English, default: `"EN"`) ### `kagi_assistant` Interact with Kagi's AI assistant models for conversations and queries. **Parameters:** - `message` (string): The message or question to send to the assistant - `model` (string, optional): AI model to use (default: uses configured default model) - `web_search` (boolean, optional): Enable web search integration (default: true) - `image` (string, optional): Base64 encoded image for vision models ## Development ### Project Structure ``` kagi-ken-mcp/ ├── src/ │ ├── index.js # Main server entry point │ ├── tools/ │ │ ├── search.js # Search tool implementation │ │ ├── summarizer.js # Summarizer tool implementation │ │ └── assistant.js # Assistant tool implementation │ └── utils/ │ └── formatting.js # Utility functions ├── package.json └── README.md ``` ### Installation 1. **Clone the repository:** ```bash git clone https://github.com/z23cc/kagi-mcp.git cd kagi-mcp ``` 2. **Install dependencies:** ```bash npm install ``` ### Running in Development Mode ```bash npm run dev ``` ### Debugging Use the MCP Inspector to debug: ```bash npx @modelcontextprotocol/inspector node ./src/index.js ``` ### Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Test with the MCP Inspector 5. Submit a pull request ## Related Projects - [czottmann/kagi-ken](https://github.com/czottmann/kagi-ken) - Unofficial session token-based Kagi client, Node - [czottmann/kagi-ken-cli](https://github.com/czottmann/kagi-ken-cli) - Unofficial Node session token-based CLI tool, Node - [Official Kagi MCP Server](https://github.com/kagisearch/kagimcp) - Python - [Model Context Protocol](https://modelcontextprotocol.io/)