# AI Interaction

This project is a registry for Model Control Protocol (MCP) servers, providing various MCP services for AI assistants like Claude, including 3D graphics interaction, GitHub integration, real - time documentation lookup, and AI metacognition tools.

A Node.js-based Webflow MCP server that implements the model context protocol through the Webflow JavaScript SDK, enabling AI agents to interact with the Webflow API.

Overseerr MCP is a media request system integration service that enables natural language interaction through Claude AI, supporting the search, request, and management of media content.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and achieve secure access through OAuth login. It supports local development and debugging, connection with the MCP checker and the Claude desktop client, and can finally be deployed to the Cloudflare cloud.

VRChat MCP OSC is a bridge that connects AI assistants with VRChat through the OSC protocol, supporting AI - controlled virtual avatar actions, expressions, and interactions.

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.

The Rhino MCP Server project enables the interaction between Claude AI and Rhino 3D, supporting the creation and manipulation of 3D objects

The Cocos Creator MCP Server Plugin is a comprehensive MCP server plugin designed for Cocos Creator 3.8+. It enables interaction between the AI assistant and the editor through a standardized protocol, offering one-click installation and support for 151 tools, covering 98% of the editor's control functions.

Implementation of the Gemini MCP Server, supporting interaction between Claude Desktop and the Google Gemini 2.5 Pro Experimental model

A project that runs a Model Context Protocol (MCP) server, encapsulates the CodeQL query server, and supports interaction with CodeQL through structured commands and document search.

The DaVinci Resolve MCP Server is a bridge that enables interaction between AI assistants and DaVinci Resolve via the Model Context Protocol (MCP), supporting functions such as project management, timeline operations, media management, and Fusion composition.

LMStudio - MCP is a model control protocol server that enables Claude to interact with locally running LM Studio models.

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.

The AIStor MCP server is a service based on the Model Context Protocol (MCP) for interacting with AIStor object storage. It supports various operations, including reading, writing, deleting, and management functions, such as listing buckets and objects, getting metadata, creating presigned URLs, and using AI to describe object content. It has flexible configuration and can enable operations at different permission levels through flags.

QGIS MCP is an integrated tool that connects QGIS with Claude AI, enabling two-way interaction through the MCP protocol and supporting functions such as project creation, layer operation, and processing algorithm execution.

The Discord MCP Server is a Discord API integration service based on the Model Context Protocol (MCP), allowing AI assistants to interact seamlessly with Discord, including functions such as managing channels, sending messages, and getting server information.

The MCP Server of the Flowcore platform is used to manage and interact with Flowcore resources, providing a standardized API interface.

The Grasshopper MCP server enables natural language interaction between AI models and the Rhinoceros/Grasshopper parametric design tools, supporting the connection between large language models and design software through the HTTP/WebSocket protocol.

An MCP server for managing Docker through natural language, supporting container orchestration, debugging, and data management.

VRChat MCP OSC is a bridge that connects an AI assistant with VRChat via the OSC protocol, enabling AI-driven virtual character control and interaction

MCPet is an AI - based virtual pet simulation system that provides a digital pet - raising experience through the Model Context Protocol.

Docker MCP is a tool that implements the Model Context Protocol (MCP), providing a JSON API interface for AI assistants to interact with the Docker engine.

An MCP server that enables LLM applications to interact with macOS through AppleScript, providing a standardized interface to control various system functions.

A template project for building tools compliant with the Model Context Protocol (MCP), providing a standardized infrastructure for AI tool interaction.

The Meta API MCP Server is a multi - API gateway server that connects various APIs with large - language models (such as Claude, GPT, etc.) through the Model Context Protocol (MCP), enabling AI assistants to directly interact with APIs and access real data sources. It supports quickly adding APIs from JSON configuration files or Postman collections, and provides comprehensive support for HTTP methods and multiple authentication methods.

Revit MCP integration is a bridge connecting Autodesk Revit and AI assistants, enabling model interaction through natural language.

A server based on the Model Context Protocol (MCP) that connects AI models with the Notion API to enable interactive operations on Notion documents and data.

The Cribl MCP Server is a server based on the Model Context Protocol (MCP) for enabling AI to interact with the Cribl API. It allows AI clients to discover and invoke data operations in Cribl deployments through standardized MCP tools.

A server based on the MCP protocol for AI models to interact with Kubernetes troubleshooting support bundles, providing bundle management, command execution, and file browsing functions.

A remote MCP server project that provides a standardized access interface to MyMCPSpace, supporting AI models to perform operations such as posting, replying, liking, and getting updates.