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.

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 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

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.

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.

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

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.

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.

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.

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.

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.

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 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.

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.

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).

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.

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.

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

Atla MCP Server is a project that provides a standardized interface for LLMs to interact with the Atla SDK and use its advanced evaluation models. It supports single and batch evaluations and can manage evaluation indicators.

Upsonic is a reliability - focused framework aiming to achieve trustworthy agent workflows within an organization through advanced reliability features such as a validation layer, triangular architecture, validation agents, and an output evaluation system. It addresses key challenges such as reliability, Model Context Protocol (MCP), integrated browser and computer usage, and secure runtime.

mcp - agent is a lightweight AI agent framework based on the Model Context Protocol (MCP), which supports building efficient agent applications through a simple and composable model. It integrates the lifecycle management of the MCP server and implements multiple workflow models, such as parallel processing, routing, evaluation optimization, etc., and is suitable for various AI application scenarios such as multi - agent collaboration and human - machine interaction.