The Strava MCP server is a middleware service that connects the Strava sports data API with language models, providing the ability to analyze, visualize sports data, and interact with social features.

DINO-X MCP is a project that enables large language models to perform fine-grained object detection and image understanding through DINO-X and Grounding DINO 1.6 API. It can achieve precise object positioning, counting, attribute analysis, and scene understanding, and supports natural language-driven visual tasks and workflow integration.

PyMOL - MCP is a tool that integrates PyMOL with Claude AI through the Model Context Protocol (MCP), enabling control of molecular visualization and structural analysis through natural language.

The Ollama MCP Server is a bridge tool that connects the local large language models of Ollama and the Model Context Protocol (MCP). It provides complete API integration, model management, and execution functions, and supports OpenAI - compatible chat interfaces and visual multimodal models.

This project integrates Graphistry's GPU-accelerated graph visualization platform with the Model Control Protocol (MCP), providing advanced graph analysis capabilities for AI assistants and large language models, and supporting multiple data formats and network analysis functions.

SolanaViz MCP is a Model Context Protocol server that enables access, analysis, and visualization of Solana blockchain data through natural language interaction, including price prediction, security assessment, and multi - wallet analysis.

NetBrain MCP is an open-source network operation and maintenance platform that connects large language models with network devices through the Model Context Protocol, enabling AI-driven network configuration, diagnosis, and management. It also provides a professional Web terminal interface and network topology visualization functionality.

This project implements a full-stack AI agent system inspired by the model context protocol, including a React front-end and a FastAPI back-end. The back-end serves as an AI coordinator, using Google Gemini to parse natural language instructions, safely call the file system, browser, or GitHub tools to perform tasks, and return the structured results to the front-end. The system focuses on software development scenarios, providing secure sandbox file operations, real-time web browsing, and repository inspection functions, and visualizing the tool call process.

BRAINS OS is an AI operating system based on modern cloud-native technologies, designed specifically for managing large language models and dedicated AI agents, providing visual workflow editing, a unified command system, and a secure deployment framework.

A model context protocol server for the SafetyCulture API that supports querying safety inspection data through natural language and provides trend analysis and visualization functions.

A Consul service discovery and mesh management tool based on the Model Control Protocol, which realizes microservice architecture analysis, problem diagnosis, and visual display through natural language interaction.