# AI interaction

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.

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

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.

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 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.

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.

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.

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.

click-mcp is a Python library that extends Click applications through Model Context Protocol (MCP) support, enabling AI agents to interact with CLI tools.

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.

An implementation of the Model Context Protocol (MCP) in Java and the Spring framework, providing a standardized interface for interacting with AI models and supporting synchronous/asynchronous communication modes.

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.

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.

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

The Emlog MCP Server is an integrated service for the Emlog blog system based on the Model Context Protocol (MCP). It provides standardized interfaces for AI assistants to interact with the Emlog blog and supports functions such as article management, category retrieval, comment operations, and microblog posting.

Obsidian MCP is a tool that interacts with the Obsidian knowledge base through the Model Context Protocol, supporting AI assistants to read, write, and manipulate notes.

MacPilot CLI is an open-source tool that allows large language models to interact with the macOS system through the MCP protocol, providing system operation functions.

A basic MCP server for AI clients to interact with etcd

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.

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.

MacPilot CLI is an open-source tool that enables large language models to interact with the macOS system through the MCP protocol, providing system operation functions.

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 is a YouTube music control server based on the Model Context Protocol, which implements music search and playback functions through an AI model and supports cross - platform operations (mainly for Chrome automation on macOS).

Bonsai-mcp is a Blender plugin that enables analysis and operation of IFC building models through IfcOpenShell, supporting interaction with LLMs like Claude to perform building information model query tasks.

An MCP service that provides MySQL database operations, supporting AI models to interact with MySQL databases through a standardized interface, including tools for querying, executing, and table management.

A Twitter integration service based on the Model Context Protocol standard, providing comprehensive Twitter function interfaces for AI assistants, including tweet acquisition, user interaction, timeline management, and media processing.

An MCP server project integrating the Spotify API, supporting interaction between AI assistants and users' Spotify data.

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.

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.