A collection of official Microsoft MCP servers, providing AI assistant integration tools for various services such as Azure, GitHub, Microsoft 365, and Fabric. It supports local and remote deployment, helping developers connect AI models with various data sources and tools through a standardized protocol.

MCP Atlassian is a Model Context Protocol server designed for Atlassian products (Confluence and Jira), supporting both cloud and on-premises deployments and providing AI assistant integration functions.

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.

MCP Email Verify is a lightweight server based on the Model Context Protocol (MCP) for real-time verification of email address validity. It checks email format, domain name validity, and deliverability through AbstractAPI and is suitable for integration into AI applications such as Claude Desktop. The project offers simple deployment and MCP compatibility, supporting Python environments.

An OAuth2 authorization server project based on Spring Boot 3 and Java 24, supporting GraalVM native images, CRaC checkpoint restoration, local AI model testing, and Docker deployment.

The Komodo MCP server is a model context protocol service used to manage Docker or Podman container deployments through AI assistants and automation tools, enabling natural - language - controlled container management and server orchestration.

The Socket MCP server is a Model Context Protocol service for dependency security scanning, providing security scores and vulnerability detection functions for software package ecosystems such as npm and PyPI, supporting AI assistant integration and multiple deployment methods.

PhonePi MCP is an open - source project that can transform your phone into a toolbox for AI assistants. It enables remote control of phone functions through the Model Context Protocol, including managing contacts, messages, settings, etc. It emphasizes local deployment and network security.

The YepCode MCP Server is a server implementing the Model Context Protocol. It can transform YepCode processes into AI tools, supports remote and local deployment, and provides functions such as code execution, environment management, and process execution.

A customized version of NextChat that creates and deploys MCP servers through chat interactions, integrates LLM models from OpenRouter, and provides functions such as tool extraction, one-click deployment, and AI system integration guides.

A MySQL database operation server based on the Model Context Protocol, providing standardized interfaces for AI models to interact with MySQL, supporting local and remote deployment, and featuring multi - user concurrency and connection pool management.

The Dynatrace Managed MCP server is an open-source project that allows AI assistants to interact with self-hosted Dynatrace Managed deployments through the Model Context Protocol (MCP), directly integrating observability data into AI-assisted workflows. It supports local and remote modes, can be configured for multiple environments, and provides the ability to query data such as problems, security, entities, SLOs, events, logs, and metrics.

A production-ready MCP server template for hosting custom AI tools and prompt templates, supporting remote deployment and local operation, helping developers quickly build AI model interaction tools.

The Gemini MCP Server is a middleware designed for the Claude desktop application, enabling image generation through Google's Gemini AI model. This project provides a convenient installation and configuration wizard, supports Docker containerized deployment, and allows users to customize generation parameters.

The Kernel MCP Server is an open - source server based on the Model Context Protocol (MCP), providing AI assistants with secure access to Kernel platform tools and browser automation capabilities. It supports cloud application deployment, headless browser control, document search, etc., and uses OAuth 2.0 authentication to ensure security.

TeamSpeak MCP is a server control tool based on the Model Context Protocol, specifically designed to allow AI models (such as Claude) to manage TeamSpeak voice servers. It provides 39 functional tools, covering all - around operations such as user management, channel control, and permission configuration. It supports multiple deployment methods (PyPI/Docker/local) to achieve automated TeamSpeak management.

The Cribl MCP Server is a server based on the Model Context Protocol (MCP) for enabling AI to interact with the Cribl API. It allows AI clients to discover and invoke data operations in Cribl deployments through standardized MCP tools.

The Alibaba Cloud Supabase MCP Server project connects Supabase projects with AI assistants through the Model Context Protocol standard, providing tools such as project management and database querying, and supporting the deployment and operation of Supabase services on the Alibaba Cloud platform.

Trellis MCP is an interface service that connects AI assistants with Trellis 3D generation models, supporting rapid generation of 3D assets through natural language and importing them into Blender. This project is based on an open - source model and requires self - deployment of the API backend. It is fast and free, but there are stability risks.

The Counsel MCP Server is an open-source tool that connects AI assistants to the Counsel strategic reasoning platform through the Model Context Protocol, supporting multi-perspective analysis and debate-style consultations, and providing two deployment methods: local and hosted.

The plugged.in App is a comprehensive web application for managing Model Context Protocol (MCP) servers, providing a unified interface to discover, configure, and use AI tools across multiple MCP servers. It supports multi - workspaces, an interactive testing platform, tool management, resource discovery, etc., can be integrated with various MCP clients, and supports self - hosted deployment.

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.

The TianGong AI Model Context Protocol (MCP) Server supports multiple communication protocols, including STDIO and Streamable Http, and provides convenient deployment and development support.

An interaction server for the Ethereum Layer2 network based on the Model Context Protocol, providing a standardized interface for AI models and supporting token deployment, transaction management, and cross - chain operations.

MCP Hub is a framework for creating and managing Model Context Protocol (MCP) servers and clients, integrating the UV tool to simplify package management and configuration. The project includes AI - related computer vision scripts and dataset processing tools, supporting rapid deployment and development.

This project provides a series of example modules using the AWS Model Context Protocol (MCP), covering multiple languages and technology stacks, including TypeScript, Python, Spring AI, etc., demonstrating the application of MCP in scenarios such as client-server communication, ECS deployment, and RAG integration.

trackio-mcp is a Python package that provides MCP (Model Context Protocol) server support for the trackio experiment tracking tool, enabling AI agents to observe and interact with trackio experiments through the MCP protocol. It can automatically enable the MCP server functionality by a simple import without modifying existing code, and supports local and Hugging Face Spaces deployment.

This project is a Model Context Protocol (MCP) server designed for Spinnaker, enabling standardized interaction between AI models and the Spinnaker deployment system, supporting intelligent decision - making, automated operation and maintenance, and continuous optimization of the CI/CD process.

Dead Internet is a high-fidelity, sovereign simulated modern Internet ecosystem built for AI agents. It provides a complete microservice architecture (including identity authentication, social, financial, search, cloud deployment, etc.), allowing AI agents to live, interact, and operate autonomously as first-class citizens, and supports external agent access through the Model Context Protocol (MCP).

Fabric Docker is an all - in - one container deployment solution that integrates the Web interface, REST API, and MCP server of the Fabric AI enhancement framework, containing over 233 preset AI prompt modes and supporting multi - model calls.

MCP Servers is a collection of servers and services for a Model Composition Platform (MCP), aiming to promote the integration and deployment of various AI/ML models and services. The project adopts a modular architecture, supporting standardized communication and scalable design, and includes various server types such as weather services.

BRAINS OS is an AI operating system based on modern cloud-native technologies, designed specifically for managing large language models and dedicated AI agents, providing visual workflow editing, a unified command system, and a secure deployment framework.

The MySQL MCP Server is an open - source tool based on the Model Context Protocol that allows Claude AI to directly access and operate the MySQL database using natural language. It provides secure permission control, a Web management interface, and multiple deployment methods, supporting functions such as query execution, database exploration, and connection switching.

The Square Model Context Protocol (MCP) Server project follows the MCP standard and provides AI assistants with the ability to interact with the Square Connect API. It supports local and remote deployment and includes a complete Square API service catalog.

The ARC MCP Server is an innovative platform that provides tools such as document assistance, API integration, project generation, and deployment support through the seamless integration of AI models and the ARC enterprise application framework, accelerating the development of enterprise-level cloud-native applications.

This project is a remote Model Context Protocol (MCP) server based on Node.js and TypeScript, deployed in Azure Container Apps, used to implement standardized communication between AI models and tools. It provides a quick start guide, supports local development, GitHub Codespaces, and Azure deployment, and includes SSE protocol implementation, SQLite database integration, and tool interaction features.

An AI agent forwarding solution based on the MCP protocol that enables unified HTTP interface management of large model services, supporting multi-MCP Server integration and rapid deployment.

The Suncture Healthcare MCP Server is a healthcare service tool based on the Model Context Protocol, providing functions such as medical information query, symptom analysis, and BMI calculation for AI assistants. It supports installation via npm or Docker deployment.

The MySQL MCP Server is a MySQL database operation service based on the MCP protocol, providing a toolset for AI models to interact with MySQL, supporting Docker deployment and various database operation functions.

Provide an MCP Server implementation for Spinnaker, supporting AI models to interact with the Spinnaker deployment system through a standardized protocol