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.

Wren Engine is a semantic engine designed for MCP clients and AI agents, providing semantic layer support to enable AI to accurately understand enterprise data models and business logic. It supports multiple data sources and is embedded in MCP clients to ensure the accuracy and governance of data interaction.

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.

An MCP server project that enables interaction between Claude Desktop and Google Gemini AI models

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.

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 MCP Terminal Server is a secure command-line interface service that provides terminal interaction capabilities for AI models while ensuring system security.

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.

Mobi MCP Server is a service based on the Model Context Protocol, used to enable the interaction between AI models and the Mobi knowledge management platform, providing functions such as search, data retrieval, content creation, and version control.

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.

The Kaltura MCP server is a service that implements the Model Context Protocol (MCP), providing AI models with standardized interaction capabilities with the Kaltura media management system, including media upload, metadata management, classification, and user management.

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

A server that implements the Model Context Protocol, connects to a PostgreSQL database, and provides a standardized database interaction protocol for AI models.

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.

The MCP Server is an interaction system based on the Model Context Protocol standard, combining IPFS distributed storage and Claude AI processing capabilities to achieve efficient interaction between models, agents, and contextual data.

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.

OmniMCP is a tool that provides rich UI context and interaction capabilities for AI models through the Model Context Protocol (MCP) and OmniParser. It focuses on achieving in - depth understanding of the user interface through visual analysis, structured planning, and precise interaction execution.

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

A secure Windows command - line MCP server that provides secure system interaction capabilities for AI models

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

The Agentic MCP Client is an independent AI agent runner that interacts with models such as Claude, BedRock, and OpenAI through MCP protocol tools, supporting autonomous cloud operation and secure system interaction.

The FalkorDB MCP server is a middleware that follows the Model Context Protocol (MCP) and is used to connect AI models with the FalkorDB graph database to implement query and interaction functions.

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.

A basic MCP application that combines a FastAPI backend and a Gradio frontend, supporting interaction with AI models through a simple interface and integrating the Google Gemini API to implement paper search and analysis functions.

MCPMonkey is a browser extension based on Violentmonkey. It realizes the interaction function between AI language models and the browser by integrating the MCP protocol server, including tools such as tab management and page style extraction.

WhatsApp Web MCP is a bridging project that connects WhatsApp Web with AI models. It provides a standardized interface through the Model Context Protocol (MCP) and supports automating and enhancing the WhatsApp interaction function.

OmniMCP is a project that provides rich UI context and interaction capabilities for AI models through the Model Context Protocol (MCP) and OmniParser. It supports functions such as visual perception, LLM planning, and action execution, enabling in - depth understanding and precise interaction with the user interface.

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.

This project provides Rust - based type definitions for the Model Context Protocol (MCP) specification, used for standardized context interaction between AI models and runtime environments.

The Nostr MCP Server is a server based on the Model Context Protocol (MCP) that enables AI models to interact with the Nostr protocol, achieving free speech interaction on the decentralized social network.

The Patchright Lite MCP Server is a lightweight browser automation service built on the Patchright Node.js SDK, providing stealthy web browsing and interaction capabilities for AI models through the Model Context Protocol.

Agentify Components is a framework for adding semantic metadata to React components, enabling AI systems and automation tools to understand component functions. It adds standardized descriptions to components through decorators and generates MCP server configurations to enable interaction between components and AI models.

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.

A TypeScript - based ADB MCP server that serves as a bridge for interaction between AI models and Android devices

MCPOmni Connect is a powerful and universal command-line interface (CLI) that serves as the gateway to the Model Context Protocol (MCP) ecosystem. It integrates multiple MCP servers, AI models, and transport protocols, providing a unified and intelligent interface that supports multi-protocol connection, AI-driven interaction, security and privacy protection, memory management, and advanced prompt processing. etc. capabilities.

MCP is a standardized protocol for interaction between AI models and applications, supporting tool invocation, resource management, and real-time data transfer.

This project demonstrates the basic client-server interaction based on the Model Context Protocol (MCP), expanding the capabilities of AI models through an independent server and providing the function of calling computing tools.

MCP Jenkins is an open-source project that bridges Jenkins with AI language models, follows Anthropic's MCP specification, and supports secure and context-aware AI interactions.

A MySQL database operation service based on the Model Context Protocol, providing a standardized MySQL database interaction interface for AI models, supporting functions such as connection management, query execution, and table structure viewing.