MCPShell

MCPShell is a Model Context Protocol (MCP) server that bridges large language models with the operating system shell. It lets AI assistants safely invoke command‑line utilities as first‑class tools, turning raw terminal commands into structured, parameterized actions that can be described, validated, and executed within a controlled environment.

The core problem MCPShell addresses is the security and reliability of running arbitrary shell commands from an LLM. By defining tools in a YAML configuration, developers can expose only the functionality they trust, enforce strict input constraints with CEL expressions, and optionally sandbox executions. This mitigates the risk of injection attacks or accidental system damage while still giving the model powerful access to local resources.

Key capabilities include:

• Declarative tool definitions: Each tool is described with a name, description, parameters (type‑checked and required), constraints, the shell command template, and optional output formatting. This makes it straightforward to add new tools without touching code.
• Parameter validation: Before a command runs, the server evaluates CEL expressions against supplied arguments. Constraints can enforce absolute paths, disallow traversal, limit recursion depth, or restrict character sets, ensuring only safe inputs reach the shell.
• Sandboxed execution: When configured, commands run inside isolated environments (e.g., Docker or other containers), providing an extra layer of isolation for sensitive operations.
• Template substitution: Parameters are injected into shell commands via Go templating, allowing dynamic command construction while keeping the syntax simple for developers.
• Broad MCP compatibility: The server speaks plain MCP, so any LLM client that implements the protocol—Cursor, VS Code’s LLM extension, Witsy, or custom clients—can consume the tools without modification.

Real‑world use cases are abundant. A developer can expose a tool to let an assistant audit disk space, or provide read‑only Kubernetes or AWS CLI wrappers so the model can query cluster state without exposing full credentials. In data science workflows, a tool could trigger Jupyter notebooks or Python scripts, enabling interactive experiment management. Even in DevOps, a tool could trigger CI pipelines with strict input checks.

Integrating MCPShell into an AI workflow is simple: define the YAML tools, configure your MCP client to point at the MCPShell binary (often via a Go run command), and refresh the client. Once registered, the assistant can invoke any defined tool by name, passing arguments as structured JSON, and receive back a formatted output. This tight coupling between LLM prompts and system actions streamlines debugging, automation, and knowledge discovery—all while keeping execution safe and auditable.