Evaluate and Improve AI Assistant Performance

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

1. Clone and build

   git clone https://github.com/mandoline-ai/mandoline-mcp-server.git
   cd mandoline-mcp-server
   npm install
   npm run build
   

2. Configure environment (optional)

   cp .env.example .env.local
   # Edit .env.local to customize PORT, LOG_LEVEL, etc.
   

3. Start the server

   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

Health

Metrics

Evaluations

Resources