MCP-Mem0 is a template implementation that provides long-term memory functions for AI agents, integrating the Model Context Protocol (MCP) server and Mem0, supporting memory storage, retrieval, and semantic search.

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.

MCPEngine is a production-grade implementation of the Model Context Protocol (MCP), providing standardized interfaces for large language models (LLMs). It supports functions such as OAuth authentication, resource management, and tool invocation, aiming to become the 'REST of the LLM world' framework.

An implementation of the Model Context Protocol (MCP) in Java and the Spring framework, providing a standardized interface for interacting with AI models and supporting synchronous/asynchronous communication modes.

The Notion MCP Server is a server implementation based on the Model Context Protocol (MCP) that allows AI assistants to interact with the Notion API through natural language, providing a complete set of tools and endpoints to read, create, and modify Notion content.

An implementation of the MCP Server for Azure DevOps, providing a standardized protocol for AI assistants to access the Azure DevOps API

The YouTube MCP Server is a standardized interface implementation that allows AI language models to interact with YouTube content through protocols, providing functions such as video information retrieval, subtitle management, channel and playlist management.

The Zen7 Payment Agent is the first practical implementation of a DePA decentralized payment agent. It adopts a multi - agent collaborative architecture, supports the A2A and MCP protocols, and provides multi - chain and multi - currency payment solutions for AI agents and Dapp applications, enabling automated cryptocurrency payments and LLM - driven intent recognition.

An implementation of the MCP server for Magento 2 that enables AI assistants to interact with Magento stores through standardized interfaces, providing REST API integration, secure authentication, and tools for common operations.

Axone's MCP server is a lightweight implementation that exposes Axone's capabilities through the standardized Model Context Protocol (MCP), serving as a gateway for AI tools to access the data universe.

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.

An implementation of a local proxy server and client based on the MCP platform, integrating multiple AI tool functions such as weather query, Google search, camera control, image generation, and intelligent dialogue, supporting modular expansion and high-performance concurrent processing.

Implementation of Novu's MCP server for AI agents to interact with the notification system

CrewAI Enterprise - level MCP Server implementation for deploying and managing AI workflows

Frida MCP is an MCP protocol implementation based on the Frida dynamic detection toolkit, providing AI systems with the ability to interact with mobile and desktop applications.

This project is a Python - based implementation of the MCP (Model Context Protocol) server and client, aiming to provide standardized interfaces for AI models, unify the tool call formats of different large - model vendors, and integrate document search functions for multiple AI frameworks.

An implementation of the MCP server based on the Duannao Cloud Flux model API, providing AI capabilities and supporting Claude Desktop integration.

Yingdao RPA MCP Server is an implementation based on the Model Context Protocol (MCP), providing a service for Yingdao AI Power and other MCP Host tools to call RPA capabilities. It supports the local mode and the open API mode, and can query and run RPA applications, upload files, manage jobs, etc.

The Flutter MCP Server is an open - source implementation of the Model Context Protocol (MCP) for the Dart/Flutter ecosystem. It provides a unified API interface for AI assistants and developer tools, supports remote calls to the Dart/Flutter development toolchain, and enables secure and programmable automated development processes.

This project is a Python-based MCP server template designed to simplify the AI-assisted development process and provide a standardized implementation of the tool interaction protocol. It includes a weather service example, embedded protocol documentation, and Cursor Rules integration, supports multiple transmission modes, and helps developers quickly build tools that comply with MCP specifications.

A research - level MCP server implementation that interacts with the Perplexity website without an API key, providing AI - driven research functions such as web search, document retrieval, API lookup, and code analysis.

This project is a research and documentation on the Model Context Protocol (MCP), including technical analysis, implementation solutions, usage guides, and other resources, aiming to comprehensively introduce the MCP ecosystem.

An MCP server implementation designed for Google Dialogflow CX, providing seamless integration between AI assistants and Dialogflow CX.

This project is an educational implementation of an MCP server, demonstrating how to build an MCP server that can be integrated with multiple LLM clients. MCP is a standardized protocol for connecting AI models with different data sources and tools.

Memgraph MCP Server is a lightweight implementation of the Model Context Protocol (MCP) server, aiming to connect the Memgraph graph database with large language models (LLMs) and provide Cypher query execution and schema information acquisition functions.

Spec Workflow MCP is a tool that guides AI to systematically complete software development through a structured workflow (requirements → design → tasks) to ensure that code implementation is consistent with business requirements.

The PostgreSQL MCP Server is an implementation based on the Model Context Protocol (MCP), which allows AI agents to interact with PostgreSQL databases through standardized interfaces.

This is a project that converts the Python version of the MySQL MCP server to a Java implementation, providing a secure MySQL database interaction interface that allows AI assistants to access database resources in a controlled manner.

This project provides a comprehensive guide for implementing a Model Context Protocol (MCP) server for Cursor AI, including basic concepts, implementation steps, code examples, and integration requirements, aiming to enhance the AI's capabilities through custom tools.

The Osmosis Agent Toolkit is a collection of development tools for interacting with the Osmosis protocol, including core functions, AI SDK integration, and MCP server implementation.

An MCP service implementation for accessing Freepik material resources and AI image generation functions

An MCP server implementation that connects an AI agent with Google Flights data, supporting flight information query, cheapest option search, time filtering, and best recommendations.

This project is an implementation of an MCP server based on OpenSearch, providing semantic memory storage and retrieval functions for LLM applications such as Claude, and supporting the connection of AI tools and data sources through a standard protocol.

The privateGPT MCP Server is a server implementation based on the Model Context Protocol (MCP), allowing users to integrate privateGPT as an agent with MCP clients, providing a secure and modular AI application development environment.

An implementation project of the MCP server for Rhino 3D, allowing AI to create and manipulate 3D objects through the model context protocol, including Python and C# components, currently in the development stage.

An MCP server implementation integrating the Perplexity AI API, providing advanced search functions and multi - model support.

The Model Context Protocol (MCP) is an open - source protocol that provides a series of reference implementations and community - developed servers. It aims to provide large language models (LLMs) with secure and controllable access to tools and data sources. These servers demonstrate the diversity and scalability of MCP, covering various functions from file system operations to database integration, from web search to AI image generation.

A Locust load test server implementation based on the Model Context Protocol, supporting AI development environment integration

A FastMCP server implementation for connecting to the Replicate API, providing standardized interfaces to access multiple AI models. Currently, it supports the image generation function and is in the early development stage.

An implementation of the DefectDojo MCP Server, providing API interaction capabilities between AI agents and vulnerability management tools