# AI interaction

This project provides an MCP service for AI assistants to interact with the Mealie recipe database, supporting access through clients such as Claude Desktop.

A collection of MCP servers provided by Props Labs for standardized interaction between AI models and external tools and services. Currently includes the MCP server for the Fireflies.ai API.

A project that implements Server-Sent Events (SSE) based on the Starlette framework, integrates the Model Context Protocol (MCP), and provides a standard interface for AI models to interact with external tools.

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.

An implementation of the MCP server for Magento 2 that enables AI assistants to interact with Magento stores through standardized interfaces, providing REST API integration, secure authentication, and tools for common operations.

An MCP server implementation based on Elixir, supporting secure interaction between AI models and local and remote resources via the SSE protocol.

KognitiveKompanion is a modern and multi-functional AI interaction interface that supports desktop environments such as KDE and provides seamless integration with OpenAI, Ollama, and AMD Ryzen AI hardware acceleration.

The AIStor MCP server is a service based on the Model Context Protocol (MCP) for interacting with AIStor object storage, supporting multiple operations including read - write, delete, management, and AI functions.

MCP Router is a Python toolkit for managing MCP servers and interacting with OpenRouter AI models, supporting multi-step workflow orchestration and intelligent task analysis.

An MCP server based on Playwright, providing browser automation capabilities for LLMs

The MySQL MCP Server is a MySQL database operation server based on the MCP protocol, providing a set of tools for AI models to interact with MySQL, supporting various database operations and data analysis functions.

A Model Context Protocol (MCP) server implemented for Microsoft OneNote, providing a standardized interface for AI language models to interact with OneNote and supporting management functions for notebooks, sections, and pages.

SwiftMCP is a Model Context Protocol (MCP) project implemented in Swift, including server and client components, used for the interaction between AI agents and Swift applications.

GCP MCP is a service protocol that allows AI assistants to manage Google Cloud Platform resources through natural language interaction

An MCP server that provides browser automation capabilities for AI models, enabling models such as Claude and Gemini to perform web browsing, searching, and interaction operations through natural language instructions.

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.

MCP Unity is a Unity Editor extension that implements the Model Context Protocol, allowing AI assistants to interact with Unity projects and providing a bridge between Unity and the Node.js server.

The Starknet MCP Server is an AI model interaction service based on the Model Context Protocol, supporting the retrieval of the latest block information from the Starknet blockchain.

mcp-chat is an open-source and general-purpose MCP client tool for testing and evaluating MCP servers and proxies. It supports command-line interaction and web mode, can connect to various MCP servers (JS/Python/Docker), and provides functions such as chat history recording, model selection, and system prompt customization to help developers debug MCP services.

This project demonstrates how to set up a remote MCP server on Cloudflare Workers and implement OAuth login functionality. It provides a complete process for local development, MCP Inspector connection, Claude desktop application integration, and deployment to Cloudflare.

A lightweight file system service built on Python and the MCP library that securely exposes the file content and metadata in pre - configured directories and integrates with Claude AI for natural language interaction.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement OAuth login functionality. It provides a complete process guide for local development, MCP inspector connection, Claude desktop application integration, and cloud deployment.

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 for use in the Claude desktop client.

The Dev Proxy MCP Server is a tool that provides Model Context Protocol (MCP) services for Dev Proxy, allowing AI assistants to interact with Dev Proxy functions through the MCP standard, including creating configurations, getting help, and discovering features.

An MCP server based on TypeScript, designed for the interaction between LLM and the Obsidian knowledge base, providing functions such as file operations, search systems, and property management.

A WhatsApp integration server based on the MCP protocol, enabling direct interaction between AI assistants and WhatsApp through the Waha API, supporting message sending, contact management, and connection status checking.

BrowserTools MCP is a powerful browser monitoring and interaction tool that enables AI to perform intelligent analysis and operations on browsers through a Chrome extension and a local server.

Frida MCP is an MCP protocol implementation based on the Frida dynamic detection toolkit, providing AI systems with the ability to interact with mobile and desktop applications.

A simple example of an MCP server that provides addition, greeting, and code analysis functions to help beginners learn the MCP protocol

This project is a Python-based multi-MCP server management framework that interacts with the Google Gemini API through the SSE protocol. It includes two major function modules: calendar management and browser automation, and supports dynamically loading multiple services through a configuration file.