developer-tools

CLI

by g0t4

Use CLI to execute system commands and scripts directly on your host using a powerful cli command line interface. Ideal

Execute system commands and scripts on the host machine.

github stars

225

Built-in security blacklistOne-command setup with npxMCP-compliant implementation

best for

  • / AI-assisted system administration
  • / Automated development workflows
  • / Interactive command-line assistance
  • / Safe AI access to local tools

capabilities

  • / Execute shell commands safely
  • / Validate commands before execution
  • / Block dangerous system operations
  • / Return command output and errors
  • / Integrate with Claude Desktop

what it does

Lets AI models execute shell commands on your local system with built-in security protections like command blacklisting and validation.

about

CLI is a community-built MCP server published by g0t4 that provides AI assistants with tools and capabilities via the Model Context Protocol. Use CLI to execute system commands and scripts directly on your host using a powerful cli command line interface. Ideal It is categorized under developer tools. This server exposes 1 tool that AI clients can invoke during conversations and coding sessions.

how to install

You can install CLI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

CLI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

runProcess renaming/redesign

Recently I renamed the tool to runProcess to better reflect that you can run more than just shell commands with it. There are two explicit modes now:

  1. mode=executable where you pass argv with argv[0] representing the executable file and then the rest of the array contains args to it.
  2. mode=shell where you pass command_line (just like typing into bash/fish/pwsh/etc) which will use your system's default shell.

I hate APIs that make ambiguous if you're executing something via a shell, or not. I hate it being a toggle b/c there's way more to running a shell command vs exec than just flipping a switch. So I made that explicit in the new tool's parameters

If you want your model to use specific shell(s) on a system, I would list them in your system prompt. Or, maybe in your tool instructions, though models tend to pay better attention to examples in a system prompt.

I've used this new design with gptoss-120b extensively and it went off without a hitch, no issues switching as the model doesn't care about names nor even the redesigned mode part, it all seems to "make sense" to gptoss.

Let me know if you encounter problems!

Tools

Tools are for LLMs to request. Claude Sonnet 3.5 intelligently uses run_process. And, initial testing shows promising results with Groq Desktop with MCP and llama4 models.

Currently, just one command to rule them all!

  • run_process - run a command, i.e. hostname or ls -al or echo "hello world" etc
    • Returns STDOUT and STDERR as text
    • Optional stdin parameter means your LLM can
      • pass scripts over STDIN to commands like fish, bash, zsh, python
      • create files with cat >> foo/bar.txt from the text in stdin

[!WARNING] Be careful what you ask this server to run! In Claude Desktop app, use Approve Once (not Allow for This Chat) so you can review each command, use Deny if you don't trust the command. Permissions are dictated by the user that runs the server. DO NOT run with sudo.

Video walkthrough

<a href="https://youtu.be/0-VPu1Pc18w"><img src="https://img.youtube.com/vi/0-VPu1Pc18w/maxresdefault.jpg" width="480" alt="YouTube Thumbnail"></a>

Prompts

Prompts are for users to include in chat history, i.e. via Zed's slash commands (in its AI Chat panel)

  • run_process - generate a prompt message with the command output
  • FYI this was mostly a learning exercise... I see this as a user requested tool call. That's a fancy way to say, it's a template for running a command and passing the outputs to the model!

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Installation

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Groq Desktop (beta, macOS) uses ~/Library/Application Support/groq-desktop-app/settings.json

Use the published npm package

Published to npm as mcp-server-commands using this workflow

{
  "mcpServers": {
    "mcp-server-commands": {
      "command": "npx",
      "args": ["mcp-server-commands"]
    }
  }
}

Use a local build (repo checkout)

Make sure to run npm run build

{
  "mcpServers": {
    "mcp-server-commands": {
      // works b/c of shebang in index.js
      "command": "/path/to/mcp-server-commands/build/index.js"
    }
  }
}

Local Models

  • Most models are trained such that they don't think they can run commands for you.
    • Sometimes, they use tools w/o hesitation... other times, I have to coax them.
    • Use a system prompt or prompt template to instruct that they should follow user requests. Including to use run_processs without double checking.
  • Ollama is a great way to run a model locally (w/ Open-WebUI)
# NOTE: make sure to review variants and sizes, so the model fits in your VRAM to perform well!

# Probably the best so far is [OpenHands LM](https://www.all-hands.dev/blog/introducing-openhands-lm-32b----a-strong-open-coding-agent-model)
ollama pull https://huggingface.co/lmstudio-community/openhands-lm-32b-v0.1-GGUF

# https://ollama.com/library/devstral
ollama pull devstral

# Qwen2.5-Coder has tool use but you have to coax it
ollama pull qwen2.5-coder

HTTP / OpenAPI

The server is implemented with the STDIO transport. For HTTP, use mcpo for an OpenAPI compatible web server interface. This works with Open-WebUI

uvx mcpo --port 3010 --api-key "supersecret" -- npx mcp-server-commands

# uvx runs mcpo => mcpo run npx => npx runs mcp-server-commands
# then, mcpo bridges STDIO <=> HTTP

[!WARNING] I briefly used mcpo with open-webui, make sure to vet it for security concerns.

Logging

Claude Desktop app writes logs to ~/Library/Logs/Claude/mcp-server-mcp-server-commands.log

By default, only important messages are logged (i.e. errors). If you want to see more messages, add --verbose to the args when configuring the server.

By the way, logs are written to STDERR because that is what Claude Desktop routes to the log files. In the future, I expect well formatted log messages to be written over the STDIO transport to the MCP client (note: not Claude Desktop app).

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

FAQ

What is the CLI MCP server?
CLI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for CLI?
This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    CLI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Piyush G· Sep 9, 2024

    We evaluated CLI against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 8, 2024

    Useful MCP listing: CLI is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Jul 7, 2024

    CLI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Jun 6, 2024

    I recommend CLI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Oshnikdeep· May 5, 2024

    Strong directory entry: CLI surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Dhruvi Jain· Apr 4, 2024

    CLI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Mar 3, 2024

    According to our notes, CLI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Pratham Ware· Feb 2, 2024

    We wired CLI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Yash Thakker· Jan 1, 2024

    CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.