mcpo is a simple and easy-to-use proxy tool that can quickly convert any MCP tool into an OpenAPI-compatible HTTP server, enabling access through standard RESTful interfaces and facilitating integration with LLM agents and applications.

This is a curated list of Web3 Model Context Protocol (MCP) servers, covering multiple categories such as chain interaction, trading, DeFi, market data, tools, and social. MCP is an open protocol that standardizes how applications provide context to LLMs, similar to the USB - C port for AI applications. DeMCP is the first decentralized MCP network, focusing on providing self - developed and open - source MCP services for agents, supporting cryptocurrency payments, and redefining the security and reliability of MCP by combining TEE and blockchain registries.

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.

This project is an MCP server for deploying code to Google Cloud Run, supporting deployment through AI agents, IDEs, and assistant applications.

MCPHub is an embeddable Model Context Protocol (MCP) solution for AI service integration. It provides a unified way of configuration and management, supports multiple AI frameworks (such as OpenAI Agents, LangChain, Autogen), and simplifies the connection between MCP servers and AI applications.

The Quickchat AI MCP server allows users to integrate Quickchat AI agents into various AI applications, such as Claude Desktop and Cursor, and achieve seamless connection through the Model Context Protocol (MCP). Users can easily configure and manage MCP services through the Quickchat AI dashboard without complex deployment.

click-mcp is a Python library that extends Click applications through Model Context Protocol (MCP) support, enabling AI agents to interact with CLI tools.

This project implements an MCP server and client for building intelligent agent applications based on Retrieval Augmented Generation (RAG). The server provides tools such as entity extraction, query optimization, and relevance checking, and the client demonstrates how to connect to the server and use these tools to enhance the performance of the RAG system.

The Pega DX MCP Server is an experimental project aiming to transform Pega Infinity™ applications into an intuitive conversational experience through the Model Context Protocol (MCP). It serves as a bridge between the Pega DX API and the natural language interface, enabling GenAI agents, IDEs, and other MCP-supported tools to interact with Pega Infinity™ applications using simple human-readable commands.

Chargebee AgentKit is a toolkit for enhancing the integration capabilities of AI applications with Chargebee and simplifying billing and subscription management.

Cyclops MCP is a tool that allows AI agents to securely manage and deploy Kubernetes applications through the Model Context Protocol, providing template validation and application creation/update functions

Neo N3 Model Context Protocol (MCP) is a standardized interface protocol for the interaction between AI agents and applications and the Neo N3 blockchain. It provides an easy - to - use server implementation, supports direct running via npx, and does not require manual environment configuration. The protocol includes functions such as blockchain information query, wallet management, asset transfer, and smart contract interaction, and additionally provides an HTTP server to expose functions in the form of RESTful APIs.

GlassTape Policy Builder is an open-source MCP server that can convert natural language security requirements into verified Cerbos YAML policies, providing zero-trust guards for AI agents and applications.

Integration of Spring Boot applications with the Goose AI agent to expand AI capabilities through the MCP protocol

MCPez is a microservice command proxy management platform that unifies the management and standardizes the backend service interfaces through a web interface, simplifying the integration and deployment of applications such as AI agents.

A Puppeteer - based webpage screenshot MCP service that supports full - page screenshots, element screenshots, multiple image formats, custom options, and authentication session management. It can be used for AI agents to visually verify web applications.

OmniMind is an open - source Python library designed to simplify Model Context Protocol (MCP) integration and support the development of AI agents, workflows, and automation. It offers plug - and - play functionality, has built - in multiple tools, and provides intelligent responses based on Google Gemini, making it suitable for developers and beginners to quickly build AI applications.

A message context protocol server integrating Apache Kafka, providing publishing and consuming functions for LLM and agent applications.

uMCP is a server implementation of the MCP protocol designed specifically for Unity, used to connect AI agents to Unity applications, with security and scalability.

This project demonstrates how to use the MCP protocol to interact with OpenAI, Azure OpenAI, and GitHub models in Azure container applications. It provides a simple terminal application demonstration for interacting with the TODO list agent. The project includes core components such as the MCP host, client, server, LLM provider, Postgres database, and toolset.

Createve.AI Nexus is an open - source project that bridges the gap between AI agents and enterprise systems by implementing the Model Context Protocol (MCP) standard. It provides secure, real - time data access and system integration capabilities. It supports multiple AI platforms, has an enterprise - level security architecture, and can connect various business applications, sensor data, and document management systems.

This project shows a low - level example demonstrating how to interact with the MCP server through a Docker image and connect to a PostgreSQL database for the LangGraph agent. MCP is an open protocol that standardizes how applications provide context to LLMs.

The Quickchat AI MCP Server is a tool that allows users to integrate the Quickchat AI agent into various AI applications, supporting seamless connection through the Model Context Protocol (MCP). Users can quickly deploy and use their Quickchat AI agents in applications such as Claude Desktop and Cursor through simple configuration.

This project is a simplified implementation based on the MemGPT research paper, built using Spring Boot and Spring AI, providing two integration methods: REST services and MCP servers, and including core agent functions and chatbot applications.

Overview: MCP is a standard protocol, similar to the Type C protocol of electronic devices (which can both charge and transmit data), enabling AI models to interact with different APIs and data sources. It aims to replace fragmented Agent code integration and improve the reliability and efficiency of AI systems. By establishing a common standard, service providers can offer their own AI capabilities according to the protocol, supporting developers to build more powerful AI applications more quickly. MCP maintains the context between different applications and services, enhancing the overall ability of autonomous task execution.

The MCP server of operative.sh is an automated web debugging agent that can autonomously execute and debug web applications, capture network traffic and error logs, and improve development efficiency.

The MCP server project provides a standardized communication protocol for tool - agent integration, facilitating AI applications to quickly invoke various tool services.

SwiftMCP is a Model Context Protocol (MCP) project implemented in Swift, including server and client components, used for the interaction between AI agents and Swift applications.

The CockroachDB MCP Server is a natural language interface designed for LLM and agent applications to manage and query CockroachDB databases, supporting integration with AI tools via the MCP protocol.

An MCP server integrated with Apache Kafka, providing message publishing and consumption functions for LLM and agent applications

The Computer Use AI SDK developed by Screenpipe is an open - source project that provides an MCP server and client for controlling computer operations through AI. It supports the native macOS environment, does not require a virtual machine, can directly interact with any application or website, and provides basic functions such as launching applications, reading content, clicking, and entering text, aiming to build automated workflows and intelligent agents.

This is a list of multiple open - source AI projects, covering multiple fields such as automated agents, large - language models, image generation, and AI development frameworks. These projects aim to help developers make money using AI technology, including building intelligent assistants, automating workflows, and content generation applications.

The Shardeum MCP Server is a powerful blockchain interaction tool that provides access to the Shardeum network through standardized RPC methods, enabling AI agents, developers, and applications to seamlessly query and interact.

The SEO Review Tools MCP Server provides advanced SEO data integration for applications and AI agents through the Model Context Protocol (MCP), supporting natural language interaction to access massive SEO data endpoints.

MCP is an open protocol for standardizing the context of AI applications, similar to the USB - C interface in the AI field, enabling seamless connection between LLMs and various data sources/tools. It provides preset integrations, multi - model switching, secure data processing, and a standardized interface, supporting the construction of LLM - based intelligent agents and complex workflows.

LayerZero OFT MCP is a model context protocol server based on TypeScript/Node.js for creating, deploying, and cross - chain bridging of Omnichain Fungible Tokens (OFTs). It abstracts the complexity of cross - chain interactions and provides a structured, context - aware layer for easy integration with LLM agents, bots, or applications that require decentralized cross - chain functionality.

This project is an MCP server for running Microsoft 365 CLI commands, designed to support the use of VS Code and GitHub Copilot Agent. In the future, it may be extended to support the development of Microsoft 365 applications.

This project demonstrates how to use the MCP protocol to interact with OpenAI, Azure OpenAI, and GitHub models in Azure container applications. It provides a simple terminal application demonstration to interact with the to - do item agent through the toolset provided by the MCP server.

An example project that includes various AI agents and RAG technologies, covering Gemini applications, MCP services, Flutter applications, and Vertex AI integration, providing AI development examples from basic to advanced.

The MCP Server for vacuum cleaner robot control launched by Ecovacs is the world's first robot service compatible with the MCP protocol. It provides four core functions: device query, cleaning control, charging control, and working status query, significantly lowering the development threshold of intelligent agent applications.