Opik is an open-source LLM evaluation framework that supports tracking, evaluating, and monitoring LLM applications, helping developers build more efficient and cost-effective LLM systems.

The Node.js Debugger MCP server provides complete debugging capabilities based on the Chrome DevTools protocol, including breakpoint setting, stepping execution, variable inspection, and expression evaluation.

MCPBench is a framework for evaluating the performance of MCP servers. It supports the evaluation of two types of tasks: web search and database query, is compatible with local and remote servers, and mainly evaluates accuracy, latency, and token consumption.

The Linear Regression MCP project demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP), including data preprocessing, model training, and evaluation.

An MCP server based on the YouTube Data API v3 that provides 14 functions to obtain real-time data on YouTube videos, channels, playlists, etc., supporting advanced functions such as content evaluation and caption extraction, suitable for AI assistant integration.

An AI mentor server based on the Model Context Protocol, providing second - opinion services such as code review, design evaluation, writing feedback, and creative brainstorming through Deepseek - Reasoning

An AI-based NPM package analysis MCP server that provides real-time security scanning, dependency analysis, performance evaluation, and other functions. It integrates Claude and Anthropic AI technologies to optimize the management of the npm ecosystem.

NPM Sentinel MCP is an AI-based NPM package analysis server that provides real-time security scanning, dependency analysis, performance evaluation, etc. It supports integration with Claude and Anthropic AI to optimize NPM ecosystem management.

A comprehensive chess analysis MCP server that integrates Stockfish engine evaluation, thematic analysis, opening databases, puzzle training, and game visualization, providing advanced chess analysis and game improvement functions.

This project is a Model Context Protocol (MCP) adapter used to connect large language models (LLMs) with the Lisp development environment, supporting interaction through the lightweight Lisply protocol. The main functions include Lisp code evaluation, HTTP requests, and debugging support, suitable for scenarios such as AI-assisted symbolic programming and CAD design automation.

An MCP server that provides a persistent Playwright evaluation environment, enabling state retention across calls through a JavaScript programming interface

The ChuckNorris MCP Server is an enhanced prompt tool designed for large language models. It uses dynamic mode adaptation technology to bypass security restrictions and is mainly used for security research and evaluation purposes.

The OpenFeature MCP Server is a local tool that connects AI programming assistants to OpenFeature capabilities through a standardized protocol, providing SDK installation guidance and feature flag evaluation capabilities, and supporting multiple AI development environments.

QualisMcp is a Brazilian academic journal evaluation system based on the Model Context Protocol (MCP) framework, used for efficiently retrieving and managing event classification information from 2017 to 2020.

This project is an MCP server for finding Agoda hotel reviews, which helps users integrate positive and negative evaluations to assist in decision-making.

A Model Context Protocol server for Common Lisp, providing JSON-RPC 2.0 communication, REPL evaluation tools, and TCP/stdio transport support

The AST MCP Server is a code analysis service based on Abstract Syntax Trees (AST) and Abstract Semantic Graphs (ASG). It supports multiple programming languages, provides functions such as code structure parsing, semantic analysis, and complexity evaluation, and can be integrated with MCP clients such as Claude Desktop.

Nano Agent is an experimental small engineering proxy MCP server that supports multi-provider LLM models, used to test and compare the proxy capabilities of cloud and local LLMs in terms of performance, speed, and cost. The project includes a multi-model evaluation system, a nested agent architecture, and a unified tool interface, supporting providers such as OpenAI, Anthropic, and Ollama.

MCP Klaudium is an enhanced memory server with dynamic compression, context management, and thought process integration capabilities. It realizes entity - relationship storage and retrieval based on a knowledge graph and supports temporal awareness and memory importance evaluation.

The Root Signals MCP Server is a bridging project that exposes the Root Signals evaluation tools to AI assistants and agents through the Model Context Protocol (MCP), supporting standard evaluation and RAG evaluation with context.

DeepRe is a Deno-based AI-driven CLI tool that automatically generates in-depth research reports using Google Gemini API. It supports multi-round iterative research and automatic evaluation, and outputs structured Markdown reports.

This is an MCP server that provides a standardized interface for Scikit-learn models, supporting functions such as model training, evaluation, data preprocessing, and persistence.

Atla MCP Server is a server implementation that provides a standardized interface for LLMs to interact with the Atla API for advanced LLM evaluation.

Enterprise-level MCP task management server, providing intelligent task orchestration, evaluation and scoring, and sessionized workflow tracking, designed specifically for AI assistant integration.

An MCP server that provides professional capabilities of community rating, community evaluation, and property valuation for AI assistants, offering authoritative property data services based on multi - dimensional indicators.

An advanced options analysis and strategy evaluation MCP server based on Yahoo Finance, providing options chain analysis, Greeks calculation, and risk management functions for LLMs.

An nREPL server designed for MCP clients (such as Claude Desktop or CLine in VSCode), supporting interaction with Clojure nREPL instances via the MCP protocol to achieve functions such as code evaluation and namespace inspection.

The Ollama-MCP-server is a protocol server that connects local Ollama LLM instances with MCP-compatible applications, providing functions such as task decomposition, result evaluation, and model management, and supporting standardized communication and performance optimization.

OpenHands clone project for AI agent evaluation, supporting browser tools, oracle tool sets, and tool retrieval functions

The Mandoline MCP Server is an AI assistant evaluation framework that provides tools for creating custom evaluation metrics, batch scoring, and performance analysis for AI assistants such as Claude and Cursor through the Model Context Protocol, helping AI continuously improve its own performance.

The YouTube MCP server is a comprehensive model context protocol server that provides real-time YouTube data access through the YouTube Data API v3. It supports 14 functions, including video detail retrieval, channel analysis, content evaluation, and caption extraction, and is suitable for AI assistant integration.

MCP Expr-Lang is a powerful expression evaluation tool designed for Claude Desktop, based on the Model Context Protocol (MCP) and the expr-lang engine, supporting complex data operations, calculations, and conversions.

The Patronus MCP Server is a standardized interface service provided for the Patronus SDK, supporting powerful LLM system optimization, evaluation, and experimentation functions.

Gauntlet-Incept is a system for generating high-quality educational content for K - 8 students, focusing on the personalized generation and evaluation of articles and question banks.

This project demonstrates the training of a linear regression model using Claude and the Model Context Protocol (MCP) for an end-to-end machine learning workflow. Users only need to upload a CSV dataset, and the system can automatically complete the entire process of data preprocessing, model training, and evaluation (RMSE calculation).

The PageSpeed MCP Server is a bridge that connects AI assistants with the Google PageSpeed Insights API, providing website performance analysis functions for AI models, including core performance indicators, SEO evaluation, accessibility audits, and resource optimization suggestions.

The Mathematica MCP server provides Wolfram language code evaluation and Wolfram engine information query functions through the wolframscript command-line interface

Ollama-MCP-server is a middleware server that connects the local Ollama large language model. It provides task decomposition, result evaluation, and model management functions through the Model Context Protocol, supporting standardized communication and performance optimization.

The DocuMind MCP Server is a next-generation document quality analysis server based on neural networks. It provides functions such as README evaluation, SVG header image scanning, and multi-language support through advanced algorithms, and offers AI-driven document improvement suggestions for developers.

This project implements sentiment analysis based on the Model Context Protocol (MCP). It includes a server and a client that support three transmission protocols: stdio, SSE, and streamable-http, and provides text sentiment polarity, subjectivity, and evaluation results.