# AI interaction

A server-side project that implements the Model Context Protocol (MCP), supporting AI assistants to interact with external services and data sources, and providing functions for task creation, running, and debugging

A model context protocol server that implements Jira integration, providing a standardized interface for AI models to interact with Jira, including REST API integration, connection pool, error handling and other functions.

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.

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

The Wonderland Editor MCP plugin interacts with AI assistants via the SSE transport protocol to enhance the development workflow.

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.

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

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.

This project demonstrates how to set up a remote MCP server on Cloudflare Workers, including OAuth login functionality, supporting local development and cloud deployment, and enabling integration with the MCP inspector and the Claude desktop application.

A message context protocol server integrating Apache Kafka, providing publishing and consuming functions for LLM and agent applications.

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.

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.

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.

Android-MCP is a lightweight open-source project that serves as a bridge between AI agents and Android devices. It enables real-world task operations such as app navigation, UI interaction, and automated testing through the MCP server, without relying on traditional computer vision or pre-set scripts.

React MCP is a service that implements the Model Context Protocol, providing Claude AI with the ability to interact with React applications, supporting the creation, running, and management of React projects, as well as file operations and terminal command execution.

A server implementing the MCP protocol for the interaction between AI assistants and the Outline document service

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.

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.

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

A Python-based MCP client and server example project, including a weather server implementation and a Claude AI interaction client. It requires an Anthropic API key to run.

The MySQL MCP Server is a MySQL database operation service based on the MCP protocol, providing a toolset for AI models to interact with MySQL, supporting Docker deployment and various database operation functions.

The Gemini MCP Server project implements the Model Context Protocol (MCP) service, enabling Claude Desktop to interact with Google's Gemini AI model. It provides full MCP protocol support, real - time response streaming, secure API key handling, and configurable model parameters, and is implemented in TypeScript.

A minimal server/client application implementation based on the Model Context Protocol (MCP) and Azure OpenAI, including the FastMCP server, Playwright testing framework, and MCP-LLM Bridge conversion function.

The MySQL MCP Server is a model context protocol server designed for MySQL databases, enabling the interaction between AI models and MySQL databases through a structured interface.

The Anytype MCP Server is a service that implements the Model Context Protocol (MCP), allowing AI assistants to interact with the Anytype API through natural language. It transforms the OpenAPI specification of Anytype into MCP tools, enabling knowledge base management through conversations.

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.

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.

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 based on Playwright, providing browser automation capabilities for LLMs

A curated list of Model Context Protocol (MCP) servers in the DevOps field, covering categories such as infrastructure as code, container orchestration, CI/CD, monitoring and observability, security, and other practical tools.