Secure Cluster MCP

Let AI coding assistants manage your SLURM cluster jobs safely.

Built with FastMCP for ML researchers who want seamless experiment management through Claude Code or other MCP-compatible agents.

Why?

Running ML experiments on HPC clusters typically means manual scp/ssh commands. This MCP server lets your AI assistant handle the workflow - transferring code, submitting jobs, monitoring progress, debugging failures - with built-in safety guardrails.

• Structure - well-defined tools for common workflows (transfer, submit, read logs)
• Guardrails - path validation, rate limiting, dangerous command blocking
• Permissions - read-only tools auto-allowed, write operations require confirmation

Recommended Claude Code permissions

In settings.local.json, auto-allow read-only tools:

{
  "permissions": {
    "allow": [
      "mcp__cluster__cluster_info",
      "mcp__cluster__list_remote",
      "mcp__cluster__check_queue",
      "mcp__cluster__read_logs",
      "mcp__cluster__search_logs"
    ]
  }
}

Tools requiring permission (write/execute): transfer_file, download_file, submit_job, poll_job, run_remote_command, singularity_test

Prerequisites

• SSH access to your cluster (key-based authentication)
• SLURM scheduler (sbatch, squeue commands)

Guardrails

• Rate limiting - max 30 commands per 5 min (configurable via env)
• Path validation - all paths must be under REMOTE_BASE_PATH
• Dangerous command blocklist - blocks rm -rf, mkfs, fork bombs, etc.
• DRY_RUN mode - set DRY_RUN=true to log commands without executing

Installation

# From GitHub
pip install git+https://github.com/FlorianSp2000/secure-cluster-mcp.git

# Or clone and install locally
git clone https://github.com/FlorianSp2000/secure-cluster-mcp.git
cd secure-cluster-mcp
pip install -e .

Configuration

Copy .env.example to .env and edit:

cp .env.example .env

Required settings:

CLUSTER_HOST=your.cluster.ip           # Cluster IP or hostname
CLUSTER_USER=your_username             # Your cluster username
REMOTE_BASE_PATH=/home/user/project/   # Your working directory on cluster
SSH_KEY_PATH=~/.ssh/your_key           # Path to SSH private key

Optional settings:

DRY_RUN=false                     # Set true to log without executing (default: false)
LOG_DIR=logs                      # Log subdirectory for job output (default: logs)
RATE_LIMIT_COMMANDS=30            # Max commands per window (default: 30)
RATE_LIMIT_WINDOW_SECONDS=300     # Rate limit window in seconds (default: 300)
LOG_TAIL_LINES=200                # Default lines to read from logs (default: 200)

Claude Code Integration

Add to ~/.claude/settings.json or .claude/settings.local.json:

If installed via pip:

{
  "mcpServers": {
    "cluster": {
      "command": "secure-cluster-mcp"
    }
  }
}

If running from cloned repo (development):

{
  "mcpServers": {
    "cluster": {
      "command": "uv",
      "args": ["--directory", "/path/to/secure-cluster-mcp", "run", "secure-cluster-mcp"]
    }
  }
}

Available Tools

Tool	Description
cluster_info	Show connection info and settings
transfer_file	Upload local file to cluster
download_file	Download file from cluster to local
submit_job	Submit sbatch script
check_queue	List user's jobs in SLURM queue
poll_job	Wait for job completion
read_logs	Read job stdout/stderr (tail)
list_remote	List files with time filtering (mmin/mtime)
search_logs	Grep log files with time filtering
run_remote_command	Execute command on login node
singularity_test	Test container on login node (no GPU, 60s cap)

Prompts

Pre-defined workflows for common tasks:

Prompt	Description
check_failed_jobs(hours)	Find errors in recent logs, summarize failures
submit_array_job(script, range)	Guide for submitting array jobs
cluster_status()	Overview of queue and recent job status
debug_job(job_id)	Debug a specific job's stdout/stderr

Time filtering with list_remote and search_logs

Both tools support time-based filtering:

• mmin=N - files modified within last N minutes
• mtime=N - files modified within last N days

# List .err files from last 24h
list_remote("logs", pattern="*.err", mtime=1)

# Search for errors in logs from last 6 hours
search_logs("Error", mmin=360)

Notes on read_logs

Can read any file under REMOTE_BASE_PATH:

# By job ID - uses LOG_DIR
read_logs("12345")  # → {REMOTE_BASE_PATH}/{LOG_DIR}/12345.out

# By full path
read_logs("/home/user/project/results/output.csv")

Troubleshooting

"Connection refused" or timeout

• Verify SSH access works: ssh user@cluster_host
• Check VPN connection if required
• Ensure SSH key has correct permissions: chmod 600 ~/.ssh/your_key

"Path not under REMOTE_BASE_PATH"

• All remote paths must be under the configured REMOTE_BASE_PATH
• Check REMOTE_BASE_PATH in your .env is correct

"Rate limit exceeded"

• Wait 5 minutes or adjust RATE_LIMIT_COMMANDS
• Rate limits persist across MCP restarts

"Log file empty or not found"

• Check LOG_DIR matches your cluster's log location
• Use full path: read_logs("/full/path/to/file.log")
• Verify job ID exists: check_queue

Commands execute but nothing happens

• Check DRY_RUN setting - must be false for real execution
• Review output for [DRY_RUN] prefix

Limitations

• SLURM only - PBS/Torque/GridEngine not supported
• Unix paths - Windows cluster paths not supported
• SSH key auth - Password authentication not supported

Development

git clone https://github.com/FlorianSp2000/secure-cluster-mcp.git
cd secure-cluster-mcp
uv sync --extra dev
uv run pytest -v

License

MIT