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.

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 Supabase MCP Server is a tool that connects Supabase projects with AI assistants. It standardizes the interaction between large language models (LLMs) and external services through the Model Context Protocol (MCP), enabling functions such as database management, configuration acquisition, and data querying.

OpenDia is an open - source browser extension tool that allows AI models to directly control the user's browser, perform automated operations using existing login status, bookmarks and other data, support multiple browsers and AI models, and focus on privacy protection.

Zen MCP is a multi-model AI collaborative development server that provides enhanced workflow tools and cross-model context management for AI coding assistants such as Claude and Gemini CLI. It supports seamless collaboration of multiple AI models to complete development tasks such as code review, debugging, and refactoring, and can maintain the continuation of conversation context between different workflows.

The Notion MCP server is a protocol service that connects AI models to the Notion workspace through the official SDK, providing a comprehensive set of Notion operation tools.

MemoryMesh is a knowledge graph server designed for AI models, focusing on text role - playing games and interactive narratives. It helps AI maintain consistent and structured memory in conversations through dynamic schema definition and automatic tool generation, enabling a richer and more dynamic interactive experience.

This project provides support for Model Context Protocol (MCP) tool calls for LangChain, enabling interaction with AI models through the MCP toolkit, such as file reading and summary generation.

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.

MCP2Lambda is an MCP protocol server that runs AWS Lambda functions as tools for large language models (LLMs). AI models can call Lambda functions to access private resources and AWS services without code modification.

MCP2Lambda is an MCP server that allows AWS Lambda functions to be used as tools for large language models (LLMs) without modifying the code. It builds a bridge between AI models and Lambda functions through the Model Context Protocol (MCP), enabling models to access private resources and perform custom operations.

The Scrapezy MCP Server is a service that enables AI models to extract structured data from websites and supports data scraping through tools such as Claude.

A Meme - generating MCP server based on the ImgFlip API, providing tool support for AI models to generate memes.

The Linkup MCP Server is a tool used to integrate Linkup's web search functionality with AI models supporting function calls through the MCP protocol.

MCPfinder is a local server tool that provides dynamic capability expansion for AI agents. By searching for and installing toolkits compatible with the MCP protocol, language models can obtain new functions on demand.

MoziChem - MCP is a collection of servers for chemical engineering and chemical applications based on the Model Context Protocol (MCP). It provides professional tools such as thermodynamic models, equation of state calculations, and phase equilibrium calculations, and supports AI assistants to perform complex chemical engineering calculations.

This project provides multiple Model Context Protocol servers, including web search, weather query, and memory storage functions, enabling AI models to integrate external tools and data sources.

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.

This project provides a set of tools and services to integrate Power Platform CLI, Graph API, and Azure REST API through the Model Context Protocol (MCP), support interaction with AI models, and simplify the development process.

A TypeScript-based MCP server template that provides sample operations and an addition tool for communication between AI models and external tools.

The MCP client project is used for advanced interaction with AI models and services, supporting tool calls and server management.

Media Gen MCP is a server that strictly follows the TypeScript and MCP specifications, focusing on generating and editing images and videos using OpenAI and Google's AI models. It provides a series of tools, including image generation/editing, video creation/remixing, file acquisition and processing, and supports intelligent resource linking and inline output. It is suitable for various MCP-compatible clients.

LOTUS-MCP is an open-source model coordination protocol that integrates the two major AI models, Mistral and Gemini, providing a unified interface, context sharing, and tool integration to achieve intelligent routing and cost optimization.

An MCP server project implemented in TypeScript based on FastMCP, used to interact with Telegram via the MTProto protocol and provide tool support for AI models.

DiffuGen is an advanced local image generation tool that integrates the MCP protocol, supports multiple AI models (including the Flux and Stable Diffusion series), and can directly generate high-quality images in the development environment. It provides flexible configuration options, multi-GPU support, and can be integrated with multiple IDEs through the MCP protocol. It also provides an OpenAPI interface for external calls.

MCP Server is an AI agent tool designed specifically for macOS that improves work efficiency through system-level control, is compatible with MCP models, and integrates AI functions.

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.

The YouTube MCP Server is a tool for obtaining and extracting subtitles from YouTube videos, enabling AI language models to access and process video content.

The MCP Context Provider is a static MCP server that provides a persistent tool context for AI models, preventing context loss between chat sessions. It automatically loads and injects tool - specific rules, syntax preferences, and best practices to ensure consistency across sessions.

Mail MCP is an email tool based on the MCP protocol that allows AI models to access email services through standardized interfaces, supporting functions such as sending, receiving, and managing emails and attachments.

Kontent.ai MCP Server is a server that implements the model context protocol, which can connect the Kontent.ai content management platform with AI tools such as Claude and Cursor, enabling the creation, management, and exploration of content models through natural language instructions.

The GitHub Repository MCP Server provides tools for AI models to access the content of GitHub repositories as context, supporting functions such as retrieving file content, repository structure, and filtering.

mcpx-py is a Python library for interacting with LLMs through the mcp.run tool. It supports multiple AI models and provides structured output functionality.

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.

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.

The TagoIO MCP Server is a tool that allows AI models to directly interact with the TagoIO IoT platform, providing functions such as device management, data analysis, platform integration, and code generation to enhance the development workflow and intelligent data analysis.

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.

Swagger MCP is an MCP server based on the Swagger specification, which helps AI generate the models required for services and MCP server functions, providing functions such as endpoint listing, model generation, and tool definition.

The Clockify MCP service is a server integrated with AI tools for managing time records in Clockify. Users can record time entries by sending prompts to large - language models.

MCP Workers AI is an SDK designed for Cloudflare Workers, used to integrate and call various MCP server tools, such as GitLab and Slack, to achieve automated operations through AI models.