Overview

mcp‑ssh‑toolkit‑py is a lightweight Model Context Protocol (MCP) server that exposes secure SSH capabilities to AI assistants such as Claude or Cline. By turning a simple Python wrapper around paramiko into an MCP service, the toolkit lets conversational agents run commands and transfer files on remote machines without exposing credentials or building custom integrations. The server acts as a bridge: the AI client sends a JSON‑encoded request, the MCP runtime executes the SSH operation, and the response is returned in a structured format that the assistant can interpret or display to the user.

The core problem it solves is the lack of a standardized, secure way for language models to perform real‑world actions over SSH. Traditional approaches require developers to write custom code, manage authentication tokens, and handle error reporting manually. With this MCP server, developers can simply register the service in their AI workflow and call pre‑defined tools like , , or . The server guarantees encrypted transport, supports both password and key‑based authentication, and does not store credentials—each request is stateless and authenticated only by the parameters supplied in the call.

Key features include:

• Command execution – Run any shell command on a remote host and receive stdout, stderr, and exit codes in the response.
• SFTP file operations – Upload or download files using standard SFTP semantics, making it trivial to transfer scripts, logs, or artifacts.
• Multiple authentication modes – Choose between username/password, RSA key files, or SSH agent forwarding.
• Configurable connection settings – Set custom ports, timeouts, and other SSH options without changing code.
• Docker‑ready deployment – A prebuilt image on Docker Hub allows instant, isolated execution in CI/CD pipelines or cloud environments.

Typical use cases span DevOps automation, where an LLM can trigger deployment scripts or check service health; system administration via chat interfaces, enabling users to issue commands and retrieve outputs directly from a conversational UI; or secure remote debugging, where the assistant can pull logs and execute diagnostics on production servers. In each scenario, the MCP server removes boilerplate, enforces secure communication, and integrates seamlessly with existing AI workflows that already understand MCP semantics.

What sets this toolkit apart is its minimal footprint combined with full compliance to the MCP specification. It leverages the official python-sdk for protocol handling while delegating SSH logic to paramiko, ensuring that developers receive a battle‑tested, well‑documented foundation. The server’s stateless design means it can be scaled horizontally behind a load balancer, and its Docker image facilitates rapid onboarding in cloud environments. For developers building intelligent assistants that need to interact with remote infrastructure, mcp‑ssh‑toolkit‑py provides a clean, secure, and extensible entry point.