Mandoline MCP Server

Mandoline MCP Server is a specialized Model Context Protocol (MCP) endpoint that empowers AI assistants such as Claude Code, Claude Desktop, Codex, and Cursor to perform self‑evaluation and continuous improvement. By exposing a set of evaluation tools, the server allows assistants to reflect on their outputs, critique alternative solutions, and iteratively refine results—all within the same conversational context. This capability transforms static code generation into a dynamic, learning loop that adapts to user preferences and project constraints.

The server acts as an intermediary between the AI client and Mandoline’s evaluation framework. When a user invokes , the assistant receives a catalog of tools that can be called to score candidate answers, compare multiple implementations, or request additional context. These tools are defined through the MCP schema and can be accessed programmatically by the assistant’s internal planner. The evaluation logic is handled on Mandoline’s side, where sophisticated metrics (e.g., correctness, performance, style adherence) are applied and returned as structured feedback. This decouples evaluation logic from the assistant, enabling rapid updates to scoring algorithms without redeploying the client.

Key capabilities include:

• Automated critique: The assistant can request a comparative analysis of several code snippets, receiving ranked results that highlight strengths and weaknesses.
• Iterative refinement: By re‑invoking evaluation tools after each iteration, the assistant can converge on an optimal solution while keeping the user informed of progress.
• Customizable scoring: Users can tailor evaluation criteria through API keys and configuration, aligning the feedback with project-specific guidelines or organizational standards.
• Cross‑platform integration: The MCP server is designed to be plug‑in for multiple development environments—Claude Code, Codex, Claude Desktop, and Cursor—making it a versatile addition to any AI‑augmented workflow.

Real‑world scenarios illustrate its value: a software engineer using Claude Code can generate several algorithmic approaches, let Mandoline evaluate them for efficiency and readability, and then receive the top‑ranked implementation. In a data science pipeline, Cursor can leverage the server to assess model training scripts for reproducibility and performance before execution. The result is a more reliable, transparent development process where the AI assistant not only produces code but also validates and improves it on the fly.

For developers, integrating Mandoline MCP is straightforward: add a single server configuration to the client’s MCP list, supply an API key, and restart the session. Once connected, the assistant automatically exposes new tools in its UI, and developers can begin calling them as part of their conversational flow. The server’s lightweight Node.js implementation ensures minimal overhead, while the hosted version removes any deployment burden entirely. In short, Mandoline MCP turns AI assistants into self‑learning partners that continuously deliver higher‑quality results.