All MCP Servers Complete list of MCP server implementations, sorted by stars
This project is a Model Context Protocol (MCP) server designed to provide real-time weather information. It integrates with the Hefeng Weather API to fetch detailed weather data, including temperature, humidity, wind speed, and precipitation. The server can be deployed using Docker and is compatible with Python 3.13.2, making it easy to set up and use in various environments.
MCPilled is a curated collection of news and updates about MCP servers, clients, protocol developments, and related events. It provides a centralized resource for tracking important milestones in the rapid evolution of the Model Context Protocol. The project also integrates tools like Supabase and OpenAI for enhanced functionality, such as vector search and article management.
This MCP server enables Claude Desktop to interact with CloudZero API, allowing users to query and analyze cloud cost data directly from the interface. It implements tools like `get_costs`, `get_dimensions`, `list_budgets`, and `list_insights` for seamless integration. The server uses JSON-RPC 2.0 for communication and can be configured to run as a background process in Claude Desktop.
This MCP server enables AI assistants to generate and edit images using Google's Gemini Flash models. It supports text-to-image generation, image transformation based on prompts, intelligent filename generation, and local image storage. The server is designed to work seamlessly with MCP clients like Claude Desktop, providing high-resolution image output and strict text exclusion features.
The CheerLights MCP Server is a Model Context Protocol (MCP) implementation that allows AI tools such as Claude to interact with the CheerLights API. CheerLights is a global IoT project that synchronizes colors across connected lights worldwide. This server provides features like fetching the current CheerLights color, viewing recent color change history, and real-time API integration. It is designed to work seamlessly with Claude for Desktop, enabling users to query CheerLights data directly through AI interactions.
The medRxiv MCP Server bridges AI assistants with medRxiv's preprint repository using the Model Context Protocol (MCP). It allows AI models to search for health sciences preprints, access detailed metadata, and retrieve paper content programmatically. Core features include paper search, efficient retrieval, metadata access, and research support, making it a valuable tool for health sciences research and analysis.
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