microsoft/markitdown
MarkItDown is a Python tool that converts various file formats into Markdown and integrates with LLM applications via an MCP server for enhanced text analysis and document processing.
Awesome MCP › Other MCP Servers
The Weather API MCP Server is a lightweight Model Context Protocol (MCP) server designed to enable AI assistants, such as Claude, to retrieve and interpret real-time weather data efficiently. This project provides a seamless integration for AI systems to access current weather information by leveraging the MCP framework, which facilitates communication between AI models and external data sources. The server supports both local and remote modes of operation, allowing users to either run the server locally with an API key from WeatherAPI or connect to a remote server endpoint. The project is built with a clear structure, including directories for command-line interface, internal server logic, MCP handlers, business logic, tools, and templates for message display. It also supports containerization through Docker, making deployment straightforward and scalable. The primary tool offered by the server is the "current_weather" function, which fetches the current weather conditions for a specified city, requiring the city name as input. The project emphasizes ease of use, with detailed instructions for installation, building from source, and running tests. It also encourages community contributions and provides guidelines for adding new features and tests. The server is licensed under the MIT License, promoting open-source collaboration. Overall, this MCP server enhances AI assistants' capabilities by providing them with up-to-date weather data, enabling more informed and context-aware interactions.
https://github.com/TuanKiri/weather-mcp-server
MarkItDown is a Python tool that converts various file formats into Markdown and integrates with LLM applications via an MCP server for enhanced text analysis and document processing.
A curated collection of Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations.
The Model Context Protocol Servers repository offers reference implementations and third-party integrations that demonstrate how MCP enables Large Language Models to securely access and interact with diverse tools and data sources.
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Headroom is a context compression layer for AI agents, reducing token usage by 60-95% while preserving accuracy, implemented as a library, proxy, and MCP server.
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Open source, self-hostable SEO platform that exposes its keyword, rank, backlink and site-audit data to AI agents through an MCP server and a set of companion agent skills.