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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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:
mode=executablewhere you passargvwithargv[0]representing theexecutablefile and then the rest of the array contains args to it.mode=shellwhere you passcommand_line(just like typing intobash/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.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
<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_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.
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 30 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★30 reviews- ★★★★★Diego Jackson· Dec 24, 2024
I recommend CLI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Advait Tandon· Dec 20, 2024
CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Advait Verma· Nov 15, 2024
Strong directory entry: CLI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Advait Nasser· Nov 11, 2024
CLI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Advait Thomas· Oct 6, 2024
Useful MCP listing: CLI is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Advait Wang· Oct 2, 2024
According to our notes, CLI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Yusuf Mensah· Sep 13, 2024
CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Oshnikdeep· Sep 5, 2024
Strong directory entry: CLI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Ganesh Mohane· Aug 24, 2024
Useful MCP listing: CLI is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Arjun Khan· Aug 4, 2024
CLI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
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