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 SSH MCP Server is a local Model Context Protocol (MCP) implementation designed to bridge LLMs and remote system administration tasks via SSH. It allows MCP clients to execute shell commands on both Linux and Windows servers, offering a robust way to automate system interactions. The server supports secure authentication methods, including password and SSH key-based access. Key features include two primary tools: 'exec' for general command execution and 'sudo-exec' for commands requiring elevated privileges. The 'sudo-exec' tool can be configured for password-protected sudo or disabled entirely, providing flexibility based on security requirements. The server also incorporates configurable timeout protection with automatic process abortion and graceful handling of hanging processes, ensuring system stability. Built with TypeScript and the official MCP SDK, it adheres to modern development practices. Installation is straightforward via npm, and it integrates seamlessly with various MCP clients like Claude Desktop, Cursor, and others, with detailed setup instructions for different authentication and configuration scenarios. The project emphasizes security, flexibility, and reliability for automated remote command execution within the MCP ecosystem.
https://github.com/tufantunc/ssh-mcp
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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