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.

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.

Unity - MCP is a bridge tool that connects large language models (LLMs) and the Unity Editor. By exposing Unity Editor functions to AI clients, it enables an AI - driven automated development process. It supports custom tool extensions and currently provides core functions such as game object management, scene operations, and asset handling. It is mainly suitable for the Unity Editor environment.

FastApply MCP Server is an enterprise-level code intelligence platform that provides comprehensive code analysis, search, and refactoring capabilities through local AI models, AST semantic search, security scanning, and intelligent pattern recognition. It supports multi-language development and large codebase processing.

MCP2Serial is a bridge project connecting physical devices with large AI models, enabling intelligent control of the Internet of Things by controlling hardware devices through natural language.

The OpenFGA MCP Server is an experimental Model Context Protocol service that enables large language models to read from and write to the OpenFGA authorization store, providing fine-grained permission management capabilities for AI agents and developers.

MCP-JaCoCo is a service tool that converts JaCoCo code coverage reports into a format suitable for large language models (LLMs), making AI-driven analysis more efficient

The GeoServer MCP Server is a server - side implementation of the Model Context Protocol (MCP) that connects large language models (LLMs) with the GeoServer REST API, enabling AI assistants to interact with geospatial data and services.

This project implements a ClickHouse MCP server for seamless integration of the ClickHouse database with AI applications such as large language models (LLMs), supporting functions such as querying databases and obtaining table structures.

dap-mcp is a Debug Adapter Protocol (DAP) implementation based on the Model Context Protocol (MCP), aiming to optimize and extend the context window of large language models to enhance the debugging workflow.

The Firebolt MCP Server is a service that implements the model context protocol, which can connect LLMs (large language models) with the Firebolt data warehouse, enabling AI assistants to directly query data, generate insights, and access documentation resources.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

Deep Research MCP is a server project based on the Model Context Protocol (MCP), aiming to conduct comprehensive network research through Tavily's search and crawl APIs and integrate the data into structured Markdown documents to support high - quality content generation for large - language models (LLMs).

BeeMCP is an unofficial MCP server used to connect the data of Bee wearable devices with large - language models (such as Claude), enabling AI assistants to access and manipulate users' personal data, including conversations, fact records, to - do lists, and location history.

The Meta API MCP Server is a multi - API gateway server that connects various APIs with large - language models (such as Claude, GPT, etc.) through the Model Context Protocol (MCP), enabling AI assistants to directly interact with APIs and access real data sources. It supports quickly adding APIs from JSON configuration files or Postman collections, and provides comprehensive support for HTTP methods and multiple authentication methods.

This project integrates Graphistry's GPU-accelerated graph visualization platform with the Model Control Protocol (MCP), providing advanced graph analysis capabilities for AI assistants and large language models, and supporting multiple data formats and network analysis functions.

The OLS MCP Server is a tool that provides ontology query services for large - language models. By connecting to the EBI Ontology Query Service API, it helps AI assistants accurately obtain ontology terms, concepts, and hierarchical structures in the biomedical field.

The MCP Code Indexer is an intelligent code retrieval tool designed specifically for large AI language models. It improves the efficiency and accuracy of code processing through semantic understanding and vectorized indexing, supporting functions such as code analysis, quality assessment, and dependency management.

dap-mcp is a DAP session management tool based on the MCP protocol, aiming to optimize and expand the context window of large language models and improve the debugging workflow.

OmniLLM is an MCP server that serves as a bridge between Claude and other large language models (such as ChatGPT, Azure OpenAI, and Google Gemini), providing a unified AI access interface.

CTX is a tool that connects code repositories with large language models (LLMs). By automatically generating structured context files, it solves the problem of low efficiency in code context transfer in AI development. It supports collecting information from various sources such as code files, Git repositories, and web pages, and can directly provide project context for AI assistants through the MCP server.

The MCP Crew AI Server is a lightweight Python - based server for running and managing CrewAI workflows, supporting communication with large language models through the MCP protocol.

Tome is a MacOS application (support for Windows and Linux is coming soon) developed by the Runebook team. It aims to simplify the use of local large language models (LLMs) with MCP servers. By integrating Ollama and managing MCP servers, users can quickly start chatting with MCP-powered models without dealing with complex configurations.