laptop-gpu-mcp

A minimal MCP (Model Context Protocol) server for running Python scripts on a remote machine over HTTP. Expose your Python environment to Claude as discoverable, schema-validated tools instead of hand-rolling SSH commands.

What this does

Exposes three tools to Claude:

• list_scripts — discover .py files in your scripts directory

• run_python — execute a script in a specific conda environment; optionally run in background and poll results

• job_status — check status or list all running jobs

Claude can call these like function calls, with automatic parameter validation and error handling.

Related MCP server: mcp-connect

Why MCP instead of SSH?

Instead of: ssh user@host 'python script.py ...' with manual parsing of output You get: Claude sees run_python(script, env, args, background, timeout) as a typed tool

Benefits:

• Claude reasons about which tool to use

• Parameters are validated before execution (e.g., rejects path traversal, invalid conda envs)

• Background jobs get tracked with job IDs that Claude can poll later

• If you add tools later, Claude discovers them automatically

Customize for your setup

Clone this repo and edit these files:

1. Configuration

Copy the example config and edit it for your machine:

cp config.example.json config.json

{
  "scriptsBaseDir": "/home/youruser/scripts",   // Where your .py files live
  "condaBaseDir": "/home/youruser/miniconda3",  // Your conda installation
  "host": "0.0.0.0",                            // Listen on all interfaces
  "port": 8420                                  // HTTP port (change if in use)
}

Every conda environment found under condaBaseDir/envs is exposed automatically — there's no allowlist to maintain. config.json is gitignored, so your paths never get committed. Every setting can also be overridden with an environment variable (SCRIPTS_BASE_DIR, CONDA_BASE_DIR, MCP_HOST, MCP_PORT) — handy for one-off runs or systemd units. Env vars take precedence over config.json.

2. Security: Firewall and auth

This server has no authentication. It's safe only on private networks.

If running on a machine you control (same network, no internet exposure):

• Allow the port through your firewall for the remote client's subnet

• Example (Linux): sudo ufw allow from 10.0.0.0/24 to any port 8420

• Example (Windows): netsh or Windows Defender firewall rules

If you need to expose this beyond a trusted network, add auth before deploying.

3. Optional: Persistent background service

To survive reboots, use systemd (Linux) or Task Scheduler (Windows). Examples in NOTES.md.

Quick start

npm install
npm run build
npm start          # Listens on http://0.0.0.0:8420/mcp

# Or for development (no build step):
npm run dev

Health check: curl http://localhost:8420/health

Connecting from Claude

Claude Code CLI

claude mcp add --transport http my-remote-python http://<server-ip>:8420/mcp

(Replace <server-ip> with your server's IP — this repo keeps real deployment addresses out of git in a gitignored address_book.json, see .gitignore)

Claude Desktop

Settings → Connectors → Add custom connector, use the same URL.

After connecting, Claude will see your three tools and can call them directly.

Example usage

Run a script in the foreground:

Claude: run_python(script="train.py", env="pytorch", args=["--epochs", "10"])
→ Blocks until done, returns stdout/stderr

Start a long job in the background:

Claude: run_python(script="preprocess.py", env="data", background=true)
→ Returns job_id immediately (e.g., "job_abc123")

Claude: job_status(job_id="job_abc123")
→ Check progress, returns status and logs

Testing

Included test scripts:

• src/hello.py — simple foreground test

• src/slow.py — long-running background test

Adapt these to verify your setup works.

Known limitations

• No persistence on crash. If the server process dies, jobs die with it. Use a systemd service or Task Scheduler to auto-restart (see NOTES.md for deployment details and known issues).

• Single machine only. This server runs Python on one remote machine. To run on multiple machines, deploy one server per machine.

• No job persistence. Job state lives only in memory; reboots lose it.

Next steps

1. Clone this repo

2. Set SCRIPTS_BASE_DIR, CONDA_BASE_DIR for your machine

3. npm install && npm run build && npm start

4. Test connectivity: curl http://your-server:8420/health

5. Add the HTTP connector to Claude Code/Desktop

6. Try calling the tools

See NOTES.md for deployment troubleshooting and operational notes.