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GPU Kill MCP Server

A MCP server for GPU Kill, enabling AI assistants and other tools to interact with GPU management functionality through a standardized interface.

Features

Resources (Read-only data)

  • gpu://list - Current GPU status and utilization
  • gpu://processes - Currently running GPU processes
  • gpu://audit - Historical GPU usage data
  • gpu://policies - Current Guard Mode policies
  • gpu://rogue-detection - Security scan results and threats

Tools (Actions)

  • kill_gpu_process - Kill a GPU process by PID
  • reset_gpu - Reset a GPU by ID
  • scan_rogue_activity - Scan for suspicious GPU activity
  • create_user_policy - Create a user policy for Guard Mode
  • get_gpu_status - Get detailed status of a specific GPU
  • kill_processes_by_name - Kill all processes matching a name pattern

Quick Start

Build and Run

# Build the MCP server
cargo build --release -p gpukill-mcp

# Run the MCP server
cargo run --release -p gpukill-mcp

# Or run with custom port
MCP_PORT=3001 cargo run --release -p gpukill-mcp

Using with AI Assistants

The MCP server exposes GPU management capabilities through a JSON-RPC interface that AI assistants can use to:

  • Monitor GPU usage and performance
  • Kill stuck or problematic processes
  • Reset crashed GPUs
  • Scan for security threats
  • Manage resource policies
  • Automate GPU operations

Example Usage

# Start the MCP server
cargo run --release -p gpukill-mcp

# The server will be available at http://localhost:3001/mcp
# AI assistants can connect and use the available tools and resources

Natural Language Examples

Ask your AI assistant to use the MCP tools with natural language:

What GPUs do I have and what's their current usage?

Kill the Python process that's stuck on GPU 0

Kill all training processes that are using too much GPU memory

Show me GPU usage and kill any stuck processes

Scan for crypto miners and suspicious activity

Create a policy to limit user memory usage to 8GB

Reset GPU 1 because it's not responding

What processes are currently using my GPUs?

API Endpoints

HTTP Interface

  • POST /mcp - Main MCP JSON-RPC endpoint
  • GET /health - Health check endpoint

MCP Methods

  • initialize - Initialize the MCP connection
  • resources/list - List available resources
  • resources/read - Read resource contents
  • tools/list - List available tools
  • tools/call - Execute a tool

Configuration

The MCP server can be configured using environment variables:

  • MCP_HOST - Bind address (default: 127.0.0.1). Use 127.0.0.1 for local-only access. Set to 0.0.0.0 only if you need remote access and have other protections (e.g. firewall, auth).
  • MCP_PORT - Port to listen on (default: 3001)
  • RUST_LOG - Logging level (default: info)

Integration

This MCP server enables AI assistants to:

  1. Monitor GPU Health: Check GPU status, utilization, and memory usage
  2. Manage Processes: Kill problematic processes or reset GPUs
  3. Security Monitoring: Scan for crypto miners and suspicious activity
  4. Policy Management: Create and manage resource policies
  5. Automation: Automate routine GPU management tasks

Development

# Run in development mode
cargo run -p gpukill-mcp

# Run with debug logging
RUST_LOG=debug cargo run -p gpukill-mcp

# Test the server
curl -X POST http://localhost:3001/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":"1","method":"tools/list","params":{}}'