Genkit is an open-source framework for building AI-driven applications, providing Node.js and Go libraries, supporting the integration of multiple AI models and vector databases, and including development tools and a plugin ecosystem.

The AWS MCP Servers are a set of dedicated servers based on the Model Context Protocol, offering various AWS-related functions, including document retrieval, knowledge base query, CDK best practices, cost analysis, image generation, etc., aiming to enhance the integration of AI applications with AWS services through a standardized protocol.

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.

Browser MCP is an MCP server + Chrome extension that enables browser automation through AI applications (such as VS Code, Claude, etc.). It features fast response, privacy protection, staying logged in, and avoiding detection.

hyper-mcp is a high-performance MCP server based on WebAssembly plugins, supporting plugin distribution through container registries and suitable for various AI application scenarios.

iMCP is a macOS application that connects users' digital lives through AI. It supports interaction with clients such as Claude Desktop via the Model Context Protocol (MCP), providing various functions including calendar, contacts, location, maps, messages, reminders, and weather.

HiveChat is an AI chat application designed for small and medium - sized teams. It supports multiple large - model service providers and provides functions such as group management, Token limits, and third - party login.

Eino is an LLM application development framework designed specifically for Golang, aiming to simplify the AI application development process through concise, scalable, reliable, and efficient component abstraction and orchestration capabilities. It provides a rich component library, powerful graphical orchestration functions, complete stream processing support, and a highly scalable aspect mechanism, covering the full-cycle toolchain from development to deployment.

Desktop Commander MCP is an AI assistant tool that enables the Claude desktop application to execute terminal commands and manage the file system. It supports functions such as code search, editing, process management, and remote file reading.

XcodeBuild MCP is a server that integrates Xcode-related tools for AI assistants and MCP clients, supporting functions such as Xcode project management, simulator control, and app utilities to achieve an automated development process.

Coco AI is a unified search platform that integrates enterprise applications and data (such as Google Workspace, Dropbox, GitHub, etc.), providing cross-platform search and AI-driven team chat functions, aiming to improve work efficiency and collaboration experience.

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.

MCP Link is an automated tool that can convert any API conforming to the OpenAPI V3 specification into an MCP server, enabling existing RESTful APIs to be seamlessly integrated into an AI-driven application ecosystem.

The Mapbox MCP Server is a model context protocol server implemented in Node.js, providing AI applications with access to Mapbox geospatial APIs, including functions such as geocoding, point - of - interest search, route planning, isochrone analysis, and static map generation.

MCP Notes is an intelligent note - taking application based on the MCP protocol, supporting AI - driven note management and web interface operations, and using DynamoDB to store data.

A local server based on the MCP protocol that implements semantic search and RAG functions for Apple Notes, which can be called by AI assistants such as Claude.

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.

Apple Doc MCP is a model context protocol server that provides direct access to Apple developer documentation. It is integrated into the AI programming assistant, supporting intelligent search, framework browsing, and detailed documentation retrieval.

The ClickUp MCP Server is a model context protocol server for integrating ClickUp tasks with AI applications, supporting task, space, list, and folder management through a standardized protocol.

EDT MCP Server is an MCP protocol server plugin for 1C:EDT, allowing AI assistants (such as Claude, GitHub Copilot, Cursor, etc.) to interact with the EDT workspace, providing functions such as project information query, error reporting, code analysis, metadata management, application debugging, tagging and grouping, and visual form screenshots.

This project provides an MCP server for deploying code to Google Cloud Run services, supporting deployment from various sources such as AI-assisted IDEs, AI assistant applications, and proxy SDKs.

A secure code sandbox environment based on Docker containers, providing an isolated execution space for AI applications.

adb - mcp is a proof - of - concept project aiming to provide an interface for LLMs through the MCP protocol to create an AI agent for controlling Adobe tools (such as Photoshop and Premiere). This project includes the MCP server, Node command proxy server, and Adobe application plugins, supporting natural language instruction operations on Adobe software and suitable for image and video editing automation.

Obsidian's MCP Tools plugin provides AI applications with the ability to securely access the knowledge base

The GPT Researcher MCP Server is an AI research server based on the MCP protocol. It can provide high-quality and optimized research results for LLM applications through in-depth web search and verification.

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.

MCP Appium is an intelligent mobile automation server based on the model context protocol, providing cross - platform mobile app testing tools for AI assistants, supporting iOS and Android platforms, and featuring intelligent element location, session management, and automated test generation functions.

Open Data MCP is an open - source project aiming to quickly connect public data sets to LLM applications through the MCP protocol. The project provides a CLI tool to achieve a 2 - minute connection (currently supporting Claude) and establishes a community collaboration framework that allows developers to easily contribute and publish public data sets. The core goal is to build an infrastructure that connects all LLM applications with millions of public data sets.

Open Data MCP is an open - source project aiming to quickly connect public data sets to LLM applications through the MCP protocol, enabling convenient access to open data and community - based publishing.

A secure Docker container code sandbox environment that provides an isolated execution space for AI applications, supporting multiple programming environments and file operations.

The ClickUp MCP Server is a model context protocol server for integrating ClickUp tasks with AI applications. It supports managing tasks, spaces, lists, folders, etc. through a standardized protocol and provides functions such as time tracking and tag management.

A service that allows AI assistants to control Apple Shortcuts through the MCP protocol

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

This project is an MCP server based on the AviationStack API. It provides query tools for real-time and future flight data, aircraft types, and detailed information, facilitating the integration of aviation data into 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.

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.

ToolJet MCP is a bridge connecting AI assistants to the ToolJet platform, enabling programmatic interaction through the Model Context Protocol and supporting functions such as user management, workspace operations, and application management.

BrowserTools MCP is a powerful browser monitoring and interaction tool that enables AI applications to capture and analyze browser data through Chrome extensions and a local Node server, and is compatible with multiple MCP clients.

The project aims to connect RAG applications to open-webui through the MCP protocol to achieve model context interaction, including basic PoC verification, knowledge base integration, and client docking.