GitHub - mandoline-ai/mandoline-mcp-server: MCP server that enables LLMs to evaluate themselves (original) (raw)
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's evaluation framework via the Model Context Protocol.
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.
Claude Code
Use the CLI to add the Mandoline MCP server to Claude Code:
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 a connected server:
Tutorial: Watch Claude evaluate multiple code solutions and pick the best one.
Official Documentation: Claude Code MCP Guide
Codex
Use the CLI to add the Mandoline MCP server to Codex:
codex mcp add mandoline --env MANDOLINE_API_KEY=sk_**** -- npx -y mcp-remote https://mandoline.ai/mcp --header 'x-api-key: ${MANDOLINE_API_KEY}'
Note: Restart any active Codex sessions after configuration changes.
Verify: Run /mcp in Codex to see Mandoline listed as a connected server:
Official Documentation: Codex MCP Configuration
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
{ "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
Cursor
Create or edit your MCP configuration file:
{ "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
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
- Clone and build
git clone https://github.com/mandoline-ai/mandoline-mcp-server.git
cd mandoline-mcp-server
npm install
npm run build - Configure environment (optional)
cp .env.example .env.local
Edit .env.local to customize PORT, LOG_LEVEL, etc.
- Start the server
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
Health
| Tool | Purpose |
|---|---|
| get_server_health | Confirm the MCP server is reachable and returning a healthy status payload. |
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
| Resource | Description |
|---|---|
| llms.txt | Mandoline docs index (tools, tutorials, blogs, leaderboards, SDKs); mirrored from https://mandoline.ai/llms.txt. |
| mcp | MCP setup guide for assistants; mirrored from https://mandoline.ai/mcp. |
Support
- Platform: https://mandoline.ai - Create account and get API keys
- Documentation: https://mandoline.ai/docs - Evaluation guides and best practices
- Issues: GitHub Issues - Bug reports and feature requests
- Email: support@mandoline.ai - Direct support
License
Apache-2.0 License - see the LICENSE file for details.