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faizbawa/mcp-remote-ssh

by Various

MCP server for remote SSH operations -- persistent sessions, structured command execution, SFTP file transfer, and port forwarding for AI agents.

F

MCP

faizbawa/mcp-remote-ssh

Added 23 June 2026

#ai-agents #ai-tools #automation #claude #cursor #devops #dotenv #environment-variables

Overview

An MCP server that enables AI agents to execute remote SSH operations including persistent sessions, structured command execution, SFTP file transfers, and port forwarding. It uses Python and exposes these capabilities through the Model Context Protocol for integration with AI assistants.

Best for

Best for
Developers prototyping AI-driven remote server automation with MCP

Use cases

  • Automate remote server management tasks via AI agents
  • Transfer files and execute commands on remote hosts programmatically
  • Set up port forwarding for secure remote access from AI tools

Notes

An MCP server that enables AI agents to execute remote SSH operations including persistent sessions, structured command execution, SFTP file transfers, and port forwarding. It uses Python and exposes these capabilities through the Model Context Protocol for integration with AI assistants.

2 stars on GitHub. Last updated 2026-06-23. Licensed MIT.

Use cases

  • Automate remote server management tasks via AI agents
  • Transfer files and execute commands on remote hosts programmatically
  • Set up port forwarding for secure remote access from AI tools

Pros

  • Supports persistent SSH sessions for long-running tasks
  • Includes SFTP and port forwarding in a single tool
  • Integrates directly with MCP-compatible AI agents

Cons

  • Very low community adoption (2 GitHub stars)
  • Limited documentation and support for production use
  • Requires Python environment and SSH credentials setup

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports persistent SSH sessions for long-running tasks
  • Includes SFTP and port forwarding in a single tool
  • Integrates directly with MCP-compatible AI agents

Cons

  • Very low community adoption (2 GitHub stars)
  • Limited documentation and support for production use
  • Requires Python environment and SSH credentials setup