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 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 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.

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.

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

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.