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.

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.

Model Context Protocol (MCP) .NET sample library, demonstrating how to use the MCP protocol to standardize the context interaction of large language models (LLMs) in .NET applications. It includes examples such as a YouTube subtitle extractor, supports flexible switching of LLM providers, and enables the construction of AI workflows.

The Unity MCP Server is a C# - based Model Context Protocol (MCP) service used to connect the Unity editor with external large - language models (LLM) or cloud - based AI agents to achieve real - time automation and intelligent interaction.

This project is a Model Context Protocol (MCP) adapter used to connect large language models (LLMs) with the Lisp development environment, supporting interaction through the lightweight Lisply protocol. The main functions include Lisp code evaluation, HTTP requests, and debugging support, suitable for scenarios such as AI-assisted symbolic programming and CAD design automation.

awesome - mcp is a project that collects Model Context Protocol (MCP) servers/clients, aiming to simplify the integration of large language models with external systems through a standardized protocol.

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.

mcp2mqtt is a bridge project that connects IoT devices with large AI models, enabling natural language control of hardware devices through the MCP and MQTT protocols.

Memgraph MCP Server is a lightweight implementation of the Model Context Protocol (MCP) server, aiming to connect the Memgraph graph database with large language models (LLMs) and provide Cypher query execution and schema information acquisition functions.

MetaMask MCP is a Model Context Protocol server that allows Large Language Models to interact with the blockchain through MetaMask, ensuring that the private key is securely stored in the user's wallet and not shared with AI agents.

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.

An AI-driven MCP server for chart and prototype drawing, integrating multiple large language models and supporting the intelligent generation of draw.io format charts and HTML interactive product prototypes in multiple styles based on natural language descriptions.

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

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.

A project based on the mcp-server, supporting large AI models to efficiently invoke Spinq's quantum computing hardware resources. It provides one-click installation scripts for Windows and macOS, automatically detecting the Python environment and installing dependencies.

Implementation of the Feishu MCP Server, providing the ability for large AI models to interact with the Feishu API, mainly supporting bitable operations.

An Excel file reading and writing service based on the Spring AI MCP framework, supporting integration with large language models for dynamic interaction

mcp2tcp is a bridge project connecting physical hardware and large AI models, realizing natural - language control of hardware devices through the MCP protocol.