MCP server
by g0t4
Use CLI to execute system commands and scripts directly on your host using a powerful cli command line interface. Ideal
Lets AI models execute shell commands on your local system with built-in security protections like command blacklisting and validation.
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.
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.
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.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
I recommend CLI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: CLI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
CLI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: CLI is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, CLI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: CLI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: CLI is the kind of server we cite when onboarding engineers to host + tool permissions.
CLI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
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runProcess renaming/redesignRecently 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:
mode=executable where you pass argv with argv[0] representing the executable file and then the rest of the array contains args to it.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 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
STDOUT and STDERR as textstdin parameter means your LLM can
STDIN to commands like fish, bash, zsh, pythoncat >> 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(notAllow for This Chat) so you can review each command, useDenyif you don't trust the command. Permissions are dictated by the user that runs the server. DO NOT run withsudo.
<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 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 outputInstall dependencies:
npm install
Build the server:
npm run build
For development with auto-rebuild:
npm run watch
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
Published to npm as mcp-server-commands using this workflow
{
"mcpServers": {
"mcp-server-commands": {
"command": "npx",
"args": ["mcp-server-commands"]
}
}
}
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"
}
}
}
run_processs without double checking.# 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
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
mcpowithopen-webui, make sure to vet it for security concerns.
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).
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.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.