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 ROS MCP Server acts as a crucial bridge connecting Large Language Models (LLMs) with ROS (Robot Operating System) and ROS2-based robots, enabling natural language control and comprehensive monitoring. Its primary advantage is facilitating this interaction without requiring any modifications to the robot's existing source code; users only need to add the `rosbridge` node. This server supports true two-way communication, allowing LLMs to both issue commands and receive real-time observational data from the robot, providing a full contextual understanding of the robot's state. It leverages the Model Context Protocol (MCP) to guide LLMs in discovering available topics, services, actions, and their types (including custom ones), ensuring correct syntax usage without manual configuration. Compatible with various MCP clients like Claude Code, Codex CLI, Gemini CLI, Claude Desktop, and ChatGPT, the ROS MCP Server extends its utility across different ROS versions, including ROS 1 and various ROS 2 distributions (e.g., Jazzy, Humble). It has been demonstrated in diverse applications, such as AI agent diagnosis of industrial robot end effectors, natural language control of mobile robots for complex tasks (like 'Grab a Coke from the fridge & go to the living room'), and controlling simulated robots like the Unitree Go2 in NVIDIA Isaac Sim. The project emphasizes ease of integration, robust communication capabilities, and deep ROS understanding for LLMs.
https://github.com/lpigeon/ros-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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