This project is a collection of learning resources about Model Context Protocol (MCP), including guides, tools, and server implementations, aiming to help developers understand and apply the MCP standard. The project provides multiple example implementations of MCP servers, such as file system operations, Git management, GitHub integration, etc., as well as development guides and practical tools.

LYRAIOS is a multi-AI agent operating system based on the Model Context Protocol (MCP), aiming to extend AI application functions by connecting financial networks and public blockchain chains, providing services such as blockchain operations, fintech analysis, and educational learning.

The Allora MCP Server is an implementation based on the Model Context Protocol (MCP) that provides the function of obtaining machine learning inference data from the Allora network, enabling AI systems to seamlessly access Allora prediction market data.

The Neural Child Development System is a breakthrough artificial intelligence framework that reconstructs the learning method of neural networks by simulating human psychological development stages (including cognitive, emotional, and social growth). This system integrates developmental psychology theories (such as Piaget's cognitive development stages and attachment theory) and neuroscience principles to build a complex architecture including perceptual processing, emotional regulation, memory systems, and psychological components, aiming to create AI with real emotional intelligence and natural development capabilities.

An interactive LeetCode MCP server that provides AI - guided authentication, learning guidance mode, code submission, and detailed feedback functions, supporting multi - language programming practice and user data tracking.

A lightweight server that exposes Mac system information through simple APIs to help AI assistants obtain real-time hardware and system data, mainly used for AI and deep learning experiments by Mac users.

Vibe Check MCP is a server that provides metacognitive supervision for AI agents. Through pattern interruptions, plan simplification, and self - learning feedback loops, it prevents AI from falling into error paths or over - designing solutions in complex tasks.

Tempera is a system that provides persistent memory for Claude Code. Through capturing coding sessions, semantic search, and reinforcement learning, it enables the AI to learn from historical experiences and continuously improve.

MCP-KG-Memory is an MCP server with a long-term memory layer based on a knowledge graph, aiming to solve the context forgetting problem of AI programming assistants. It persistently stores project goals, constraints, strategies, and user preferences through Neo4j, enabling semantic retrieval and active learning, and endowing the AI assistant with continuous memory and context awareness capabilities.

An MCP server that allows AI assistants to access and search local history session records of multiple AI coding assistants, facilitating the review, learning, and continuation of previous work.

An IMAP email server based on the MCP protocol, providing email checking, processing, and user preference learning capabilities for AI assistants.

This project guides developers on how to integrate the MCP service with GitHub Copilot to extend the capabilities of the AI-assisted development workflow. Through less than an hour of learning, users will set up the GitHub MCP server, implement Copilot proxy mode integration, and complete the full process practice from issue research to code submission.

YouTube MCP is an AI-based solution designed to enhance the YouTube content interaction experience through machine learning technology. It supports video search, subtitle access, and semantic search without using the official API.

The MCP Dual Cycle Reasoner is a metacognitive enhancement tool designed for autonomous AI agents. Through a dual-cycle framework (sentinel monitoring and arbiter management), it enables anomaly detection and experience learning to enhance the self-awareness and reliability of agents.

Orchestro is an AI development coordination platform that connects product managers, developers, and AI, transforming product ideas into production code. It coordinates the entire development process through task decomposition, dependency tracking, pattern learning, and real-time progress visualization, providing functions such as project management, task execution, knowledge base, and real-time dashboard.

MCP Serve is a powerful deep learning model server tool that supports deployment through Shell execution, Ngrok connection, or Docker containers, and integrates multiple advanced AI technologies.

ToolFront is an AI proxy database connection tool that securely connects coding assistants to team databases through the MCP protocol, enabling collaborative learning and sharing of query patterns.

An MCP server that helps users with metacognition by saving questions in LLM learning sessions. It focuses on recording users' questions rather than AI answers, and tracks the learning journey and knowledge gaps in a lightweight way.

Personal learning project aiming to understand protocol design, client - server communication, and modular architecture through implementing a module protocol server.

Resource library for the MCP server seminar, containing learning, setup, and practice content for the MCP server, aiming to help participants master the basic knowledge and applications of the MCP server.

The Canvas LMS MCP server is a bridge connecting an AI system and the Canvas Learning Management System, providing access to educational data such as courses, assignments, and quizzes.

This is a Python - based custom MCP (Model Context Protocol) server demonstration project, aiming to show how to build a server that uses a custom protocol for communication. It is suitable for learning network programming and protocol design.

The MCP Server Starter is a server startup project that provides learning resources for AI - assisted software development and is associated with multiple developer tool projects.

An intelligent documentation server that provides AI-assisted code improvement and document management functions through integration with Claude Desktop, supports multiple development frameworks, and has learning capabilities.

Nexus Memory MCP is a persistent semantic memory system designed for AI agents. It can store and retrieve memories semantically, support knowledge graphs and pattern learning, and provide pre - built domain professional knowledge packs to achieve the identity continuity of agents.

The MCP Analytics Suite is an intelligent analytics platform that automatically transforms business problems described in natural language into actionable insights through AI-driven intelligent discovery. It provides an end-to-end analysis workflow, supports various statistical methods and machine learning techniques, and integrates secure data processing and interactive reporting features.

A personal learning project that builds a small AI agent by integrating the X API with the MCP server to explore backend integration and AI workflows, with an emphasis on security and ethical practices.

Brain-MCP is an MCP service that provides a comprehensive memory management system for AI, simulating the human thinking process, supporting short-term memory caching, long-term memory associated storage, and thinking chain management, enabling continuous learning and experience accumulation of AI.

This is a generative AI course project for beginners, providing a complete learning path from environment setup to practical application. The project includes 6 concept courses and 6 programming courses, using the Azure OpenAI service for practical operations. Learners can quickly start through GitHub Codespaces or choose to install the Python environment locally. The project emphasizes the secure management of API keys and provides detailed setup guides. In addition, learners are encouraged to participate in open-source contributions, and an AI community communication platform is provided.

An MCP service that transforms YouTube into a queryable knowledge source, providing functions such as video search, detail retrieval, and content analysis, and supporting advanced operations such as summary generation and learning path planning through AI.

The remote MCP service of Academiadepolitie.com, a platform for preparing for the entrance exams of the Romanian Police Academy, provides AI-driven personalized learning analytics, real-time data access, and secure authentication for over 50,000 students, covering 20 legal and policing disciplines.

MCP Sigmund is a financial data analysis and learning tool based on a PostgreSQL database. It provides intelligent formatted financial data query services for AI assistants through the Model Context Protocol and is only for educational purposes.

An MCP server implemented based on Spring AI and Oracle database, mainly used for learning and testing purposes

The GPTers Search MCP Server is an MCP server that provides knowledge search services for the GPTers AI learning community, supporting the retrieval of community content through the MCP protocol to clients.

StateCLI is an infrastructure that provides a memory and self - awareness layer for AI agents. By tracking, replaying, and undoing operations, it endows agents with the abilities of memory, error repair, and historical learning.

This project is a GitHub repository for testing and exploring the implementation of the Model Context Protocol (MCP) server, containing sub - projects related to learning multiple programming languages, DevOps tools, game development, and AI.