# Tool integration

An independent implementation of the IOG Model Context Protocol (MCP) server that provides tool registration and data retrieval functions and supports integration with the Windsurf system.

The MCP server project provides a standardized communication protocol for tool - agent integration, facilitating AI applications to quickly invoke various tool services.

A developer helper tool named COCO that provides code review and pair programming assistance functions, and can help developers improve code quality through questions.

aiohttp-mcp is a library based on aiohttp for building server tools compliant with the Model Context Protocol (MCP) standard. It supports asynchronous design and type hints, facilitating the rapid development of MCP services.

This project is a Python-based MCP server for integrating custom tools in AI-assisted development environments, providing addition operations and API key retrieval functions.

Pro MCP is a community-driven directory of Model Context Protocol (MCP) servers, aiming to provide a comprehensive list of MCP servers for developers and AI enthusiasts and promote the development of intelligent applications.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function, supporting connection and use through the MCP Inspector and Claude Desktop clients.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement OAuth login functionality. It supports local development and debugging, connection testing via the MCP Inspector, and integration into the Claude desktop application to call math tools. Finally, it provides a complete process for deployment to Cloudflare and remote connection.

This project demonstrates how to build a remote MCP server on Cloudflare Workers and implement the OAuth login function. It supports local development and debugging, connection testing through the MCP Inspector, and integration into the Claude Desktop application to invoke mathematical tools. Finally, it can be deployed to the Cloudflare cloud for remote client access.

This project provides a series of Model Context Protocol (MCP) servers for enhancing the external capabilities of AI assistants, including independent implementation modules for specific functions such as educational course queries.

A lightweight MCP server implementation that exposes custom tools via SSE for AI client integration.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function. It supports local development and debugging, connection testing through the MCP Inspector, and scenarios such as integrating the Claude desktop application to call mathematical tools. Finally, it can be deployed to the Cloudflare cloud service.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function. It supports local development and debugging, connection testing through the MCP inspector, and integration into the Claude desktop application to call math tools. Finally, it guides you on how to deploy to Cloudflare and access it remotely.

StatusNow.Dev provides a comprehensive website and server monitoring solution, including SSL/domain expiration monitoring, website availability, response time, etc.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement OAuth login functionality. It supports local development and debugging, connection testing through the MCP Inspector, and integration into the Claude desktop application to call math tools. Finally, it provides a complete process for deploying to Cloudflare and remote connection.

The Gentoro MCP server supports Gentoro services, enabling Claude to interact with the Gentoro bridge and its underlying functions.

The MCP startup project is an AI application development framework that includes a Python server - side and a TypeScript client - side, supporting the interaction between AI models and external tools/APIs, and providing functions such as document search and conversation management.

The MCP LLM Bridge is a bridging tool that connects the Model Context Protocol (MCP) server with OpenAI-compatible LLMs. It enables bidirectional protocol conversion between MCP and the OpenAI function call interface, supporting both cloud and local model calls.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function. It supports local development and debugging, connection testing through MCP Inspector, and integration into the Claude desktop application for using mathematical tools.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function. It supports local development and debugging, connection testing through the MCP Inspector, and integrating the Claude desktop client into the MCP service for math tool calls. Finally, it provides a complete process for deploying to Cloudflare and remote connection.

This project is a collection of reference implementations for the Model Context Protocol (MCP), including MCP server implementations for various tools and services, aiming to provide secure and controllable access to tools and data sources for large language models (LLMs).

The Gentoro MCP server supports the Gentoro service, enabling Claude to interact with the Gentoro bridge and its underlying functions. Users can create and manage tool bridges through their Gentoro accounts and integrate them into agents such as Claude via API keys.

This project is a remote Model Context Protocol (MCP) server based on Node.js and TypeScript, deployed in Azure Container Apps, used to implement standardized communication between AI models and tools. It provides a quick start guide, supports local development, GitHub Codespaces, and Azure deployment, and includes SSE protocol implementation, SQLite database integration, and tool interaction features.

AWorld is a multi - agent system framework aiming to bridge the gap between theoretical MAS capabilities and practical applications, providing a full - set solution from single - agent to multi - agent collaboration/competition. The project supports scenarios such as browser/mobile operations and GAIA benchmark testing, adopts a client - server architecture, integrates a rich toolchain, and includes performance evaluation and training functions.

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.

An MCP protocol server that provides access to the tools on the Toolhouse platform

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.

MCPilot is an AI chatbot that integrates real-time tools (such as DuckDuckGo, Airbnb, and weather APIs), providing context-aware real-time queries and task assistance through the Gradio front-end and GROQ backend.

The plugged.in App is a comprehensive web application for managing Model Context Protocol (MCP) servers, providing a unified interface to discover, configure, and use AI tools across multiple MCP servers. It supports multi - workspaces, an interactive testing platform, tool management, resource discovery, etc., can be integrated with various MCP clients, and supports self - hosted deployment.

Model Context Protocol (MCP) is an open-source protocol that provides a series of reference implementations and community-developed servers, enabling large language models (LLMs) to securely and controllably access tools and data sources. The project includes various types of server implementations covering multiple domains such as file systems, databases, Git, and Slack, demonstrating the diversity and scalability of MCP.