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.

An MCP server that integrates AI models with WinDbg/CDB for analyzing Windows crash dump files and remote debugging, supporting natural language interaction to execute debugging commands.

The AI Infrastructure Agent is an intelligent system that allows users to manage AWS cloud resources through natural language commands. It uses AI models to convert user requirements into executable AWS operations and provides web dashboards, state management, and security protection functions.

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.

The YouTube MCP Server is a standardized interface implementation that allows AI language models to interact with YouTube content through protocols, providing functions such as video information retrieval, subtitle management, channel and playlist management.

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.

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.

The ScrapeGraph MCP Server is a production - ready model context protocol server that provides seamless integration with the ScrapeGraph AI API, enabling language models to leverage advanced AI - driven web scraping capabilities.

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.

The YouTube MCP Server is a service that implements the Model Context Protocol (MCP). It provides a standardized interface for AI language models to interact with YouTube content, supporting functions such as video information retrieval, subtitle management, channel and playlist operations, etc.

ngspice-mcp is an MCP protocol-based server that provides a standardized interface for AI language models to access ngspice circuit simulation capabilities, supporting operations such as circuit loading, simulation execution, and data acquisition.

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.

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.

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.

Okta MCP Server is a server based on the Model Context Protocol that allows large - language models and AI agents to manage Okta organizations through natural language instructions, supporting management operations such as users, groups, applications, policies, and logs.

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.

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 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 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.

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.

This is an AWS Amplify Data MCP Server project that allows interaction with the data models of Amplify Gen2 applications through natural language, supports operations such as querying and modifying, and needs to be used with AI assistants like Claude.

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.

The YouTube data MCP server enables interaction between AI language models and YouTube content through a standardized interface

A code analysis server based on the MCP protocol that helps AI models understand and analyze codebases through natural language conversations, providing lightweight code exploration and understanding capabilities.

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.

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

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 Perplexity MCP Server is a Node.js-based server that implements Anthropic's Model Context Protocol (MCP) for connecting Claude and Perplexity's language models, providing enhanced AI interaction tools.

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.

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.

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.

The Local MCP Tools Collection aims to simplify the integration of multi - functional tools for local large language models, supporting multiple MCP servers through a single virtual environment, including web search, Python sandbox, and long - term memory functions.

An MCP server that provides Ruby console functionality, allowing AI assistants to execute Rails console, IRB, or Racksh commands through natural language, query models, and interact with Ruby/Rails applications, supporting persistent sessions.

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.