MCP server testing & eval framework with LLM-as-a-judge

A Playwright-based framework for testing and evaluating MCP servers using LLMs as judges, enhancing agent quality.

MCP Server Testing & Evaluation Framework with LLM-as-a-Judge

This framework provides a robust solution for testing and evaluating your MCP (Multi-Agent Conversation Protocol) servers. Leveraging Playwright for browser automation and Large Language Models (LLMs) as judges, it automates the assessment of agent performance and conversation quality. This tool is designed for developers actively building and refining AI agents that interact within an MCP environment.

What it Does

The core function of this framework is to simulate user interactions with your MCP server and objectively evaluate the responses generated by your AI agents. It automates the process of sending prompts, receiving agent replies, and then using an LLM to judge the quality, relevance, and correctness of those replies. This allows for rapid iteration and improvement of agent logic and conversational capabilities.

Key Features

Playwright Integration: Utilizes Playwright for reliable browser automation, enabling seamless interaction with web-based MCP server interfaces.
LLM-as-a-Judge: Employs LLMs to act as evaluators, providing nuanced and context-aware assessments of agent responses, going beyond simple keyword matching.
Automated Testing: Facilitates the creation and execution of automated test suites to consistently measure agent performance across various scenarios.
Evaluation Metrics: Supports the definition and tracking of key evaluation metrics to quantify agent quality and identify areas for improvement.
Developer-Focused: Built with developers in mind, offering a technical and direct approach to testing and evaluation.

Who it's For

This tool is specifically designed for AI developers , ML engineers , and researchers working on multi-agent systems, particularly those utilizing the Multi-Agent Conversation Protocol (MCP). If you are building, deploying, or optimizing AI agents that require sophisticated conversational abilities and need a reliable method for testing and evaluating their performance, this framework will be invaluable. It's ideal for projects where agent quality and the effectiveness of conversations are critical success factors.