# Mandoline MCP Server Enable AI assistants like Claude Code, Claude Desktop, and Cursor to reflect on, critique, and continuously improve their own performance using [Mandoline](https://mandoline.ai)'s evaluation framework via the [Model Context Protocol](https://modelcontextprotocol.io). --- # Client Setup **Most users should start here.** Use Mandoline's hosted MCP server to integrate evaluation tools into your AI assistant. For each integration below, replace `sk_****` with your actual API key from [mandoline.ai/account](https://mandoline.ai/account). ## Claude Code Use the CLI to add the Mandoline MCP server to Claude Code: ```bash claude mcp add --scope user --transport http mandoline https://mandoline.ai/mcp --header "x-api-key: sk_****" ``` You can use `--scope user` (across projects) or `--scope project` (current project only). **Note**: Restart any active Claude Code sessions after configuration changes. **Verify**: Run `/mcp` in Claude Code to see Mandoline listed as an active server. **Official Documentation**: [Claude Code MCP Guide](https://docs.anthropic.com/en/docs/claude-code/mcp) ## Claude Desktop Edit your configuration file (**Settings > Developer > Edit Config**): - **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` - **Windows**: `%APPDATA%/Claude/claude_desktop_config.json` ```json { "mcpServers": { "Mandoline": { "command": "npx", "args": [ "-y", "mcp-remote", "https://mandoline.ai/mcp", "--header", "x-api-key: ${MANDOLINE_API_KEY}" ], "env": { "MANDOLINE_API_KEY": "sk_****" } } } } ``` This configuration applies globally to all conversations. **Note**: Restart Claude Desktop after configuration changes. **Verify**: Look for Mandoline tools when you click the "Search and tools" button. **Official Documentation**: [MCP Quickstart Guide](https://modelcontextprotocol.io/quickstart) ## Cursor Create or edit your MCP configuration file: ```json { "mcpServers": { "Mandoline": { "url": "https://mandoline.ai/mcp", "headers": { "x-api-key": "sk_****" } } } } ``` You can use your global configuration (affects all projects) `~/.cursor/mcp.json` or project-local configuration (current project only) `.cursor/mcp.json` (in project root) **Note**: Restart Cursor after configuration changes. **Verify**: Check the Output panel (Ctrl+Shift+U) → "MCP Logs" for successful connection, or look for Mandoline tools in the Composer Agent. **Official Documentation**: [Cursor MCP Guide](https://docs.cursor.com/context/model-context-protocol) --- # Server Setup **Only needed if you want to run the server locally or contribute to development.** Most users should use the hosted server above. **Prerequisites:** Node.js 18+ and npm ## Installation 1. **Clone and build** ```bash git clone https://github.com/mandoline-ai/mandoline-mcp-server.git cd mandoline-mcp-server npm install npm run build ``` 2. **Configure environment (optional)** ```bash cp .env.example .env.local # Edit .env.local to customize PORT, LOG_LEVEL, etc. ``` 3. **Start the server** ```bash npm start ``` The server runs on `http://localhost:8080` by default. ## Using Local Server To use your local server instead of the hosted one, replace `https://mandoline.ai/mcp` with `http://localhost:8080/mcp` in the client configurations above. --- # Usage Once integrated, you can use Mandoline evaluation tools directly in your AI assistant conversations. ## Tools ## Metrics | Tool | Purpose | | ---------------------- | --------------------------------------------------------- | | `create_metric` | Define custom evaluation criteria for your specific tasks | | `batch_create_metrics` | Create multiple evaluation metrics in one operation | | `get_metric` | Retrieve details about a specific metric | | `get_metrics` | Browse your metrics with filtering and pagination | | `update_metric` | Modify existing metric definitions | ## Evaluations | Tool | Purpose | | -------------------------- | ------------------------------------------------------- | | `create_evaluation` | Score prompt/response pairs against your metrics | | `batch_create_evaluations` | Evaluate the same content against multiple metrics | | `get_evaluation` | Retrieve evaluation results and scores | | `get_evaluations` | Browse evaluation history with filtering and pagination | | `update_evaluation` | Add metadata or context to evaluations | ## Resources Access Mandoline's documentation and reference materials directly in your AI assistant, including model comparison guides and evaluation best practices. --- # Support - **Platform**: [https://mandoline.ai](https://mandoline.ai) - Create account and get API keys - **Documentation**: [https://mandoline.ai/docs](https://mandoline.ai/docs) - Evaluation guides and best practices - **Issues**: [GitHub Issues](https://github.com/mandoline-ai/mandoline-mcp-server/issues) - Bug reports and feature requests - **Email**: support@mandoline.ai - Direct support --- # License Apache-2.0 License - see the [LICENSE](LICENSE) file for details.