Airweave is an open - source context retrieval layer for AI agents and RAG systems. It connects and synchronizes data from various applications, tools, and databases, and provides relevant, real - time, multi - source contextual information to AI agents through a unified search interface.

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.

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.

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.

This is an experimental MCP server that allows AI agents to interact with running Flutter applications through the VM service and execute Maestro automated testing processes.

A high-performance MCP server implemented in Go language, providing AI agents and applications with the ability to query and access the Redfish API through natural language, enabling intelligent management and monitoring of data center infrastructure.

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.

Okta MCP Server is a server based on the Model Context Protocol that allows large - language models and AI agents to manage Okta organizations through natural language instructions, supporting management operations such as users, groups, applications, policies, and logs.

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.

RegenNexus UAP is a universal adapter protocol for connecting devices, robots, applications, and AI agents, providing low-latency, high-security communication, and supporting multiple hardware and MCP integrations.

BNBChain MCP is a server based on the Model Context Protocol (MCP), providing secure interaction between AI agents and applications with the Binance Smart Chain (BSC) ecosystem. It integrates functions such as wallet management, token transfer, PancakeSwap decentralized exchange operations, Meme token trading, and GoPlus security analysis. It is developed using TypeScript and supports seamless integration with Claude Desktop.

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.

An MCP server based on the Java Debug Interface, supporting AI agents to debug Java applications at runtime, including functions such as breakpoint management, state checking, and dynamic modification.

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.

Flutter - skill is an AI - native end - to - end testing tool that allows AI agents to directly interact with running applications through the MCP protocol, supporting 10 platforms without writing test code.

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.

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

This is a server based on the Model Context Protocol (MCP), specifically designed to provide an access interface to the OpenStreetMap tagging knowledge base for AI agents and LLM applications, supporting functions such as tag query, preset discovery, and validation.

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.

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.

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 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.

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 is a Node.js server based on the Model Context Protocol (MCP). It encapsulates the Appium mobile automation capabilities into MCP tools, enabling LLM intelligent agents to control Android or iOS applications through natural language instructions, achieving automated testing and interaction.

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