About Mcp Server Commands
Mcp Server Commands is an MCP server that runs commands on the host machine and returns their stdout and stderr. It suits users who want a model to carry out command line operations, and it runs locally over STDIO.
Key features
- 01Runs commands through the runProcess tool
- 02Accepts either a command_line string or an argv array
- 03Returns STDOUT and STDERR as text
- 04Passes optional stdin to shells and scripts
- 05Describes HTTP access through mcpo bridging
Use cases
- 01Letting a model query host information such as hostname or ls
- 02Sending scripts over stdin to bash or python
- 03Performing local file operations from a local model via the command line
MCP support
The project is an MCP server that exposes a run_process tool to MCP clients, executing commands on the host and returning their output. It uses the STDIO transport.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMErunProcess tool
The runProcess tool runs processes on the host machine. There are two mutually exclusive ways to invoke it:
command_line(string) — Executed via the system's default shell (just like typing intobash/fish/pwsh/etc). Shell features like pipes, redirects, and variable expansion all work.argv(string array) — Direct executable invocation.argv[0]is the executable, the rest are arguments. No shell interpretation.
You cannot pass both. The tool infers whether to use a shell from which parameter you provide.
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.
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.hostnameorls -alorecho "hello world"etc- Returns
STDOUTandSTDERRas text - Optional
stdinparameter means your LLM can- pass scripts over
STDINto commands likefish,bash,zsh,python - create files with
cat >> foo/bar.txtfrom the text instdin
- pass scripts over
- Returns
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.
Video walkthrough
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_processswithout 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
mcpowithopen-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.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
