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Thunder Compute provides an MCP (Model Context Protocol) server that lets AI coding agents manage GPU instances on your behalf. Create, monitor, modify, and tear down instances without leaving your agent workflow.

Prerequisites

  1. A Thunder Compute account
  2. An AI agent that supports remote MCP servers (Claude Code, Cursor, Codex, etc.)
No local installation or API tokens required — authentication is handled via OAuth in your browser.

Setup

Run this in your terminal:
claude mcp add --transport http thunder-compute https://www.thundercompute.com/mcp
Then start Claude Code and run /mcp to authenticate. A browser window will open for you to log in and authorize access.
Alternatively, add to ~/.claude.json (global) or .claude.json in your project root:
{
  "mcpServers": {
    "thunder-compute": {
      "url": "https://www.thundercompute.com/mcp"
    }
  }
}

Authentication

No API tokens or environment variables needed. When you first connect, a browser window opens for you to log in with your Thunder Compute account and authorize access. Tokens are refreshed automatically — you only authenticate once per session.

Available Tools

Instance Management

ToolDescription
list_instancesList all GPU instances with status, IP, and configuration
create_instanceCreate a new GPU instance (specify GPU type, template, mode, etc.)
delete_instanceDelete an instance (irreversible)
modify_instanceChange instance config (GPU type, vCPUs, disk, mode)
run_commandExecute a shell command on a running instance and return stdout, stderr, and exit code

Information

ToolDescription
get_specsGet available GPU specs (VRAM, vCPU options, storage ranges)
get_availabilityGet current GPU availability status for each spec
get_pricingGet current per-hour GPU pricing
list_templatesList available OS templates (Ubuntu, PyTorch, etc.)

Snapshots

ToolDescription
list_snapshotsList all instance snapshots
create_snapshotCreate a snapshot of an instance
delete_snapshotDelete a snapshot (irreversible)

SSH Keys

ToolDescription
list_ssh_keysList SSH keys in your organization
create_ssh_keyAdd an SSH public key to your organization
delete_ssh_keyDelete an SSH key
add_ssh_key_to_instanceAdd an SSH public key to a running instance’s authorized_keys

Port Forwarding

ToolDescription
list_portsList all instances with their forwarded ports
forward_portForward HTTP ports on an instance
delete_portRemove forwarded ports from an instance

Connectivity

ToolDescription
get_ssh_commandGet the SSH command to connect to an instance
get_scp_commandGet the SCP command to copy files to/from an instance

Billing & Usage

ToolDescription
get_meter_dataGet GPU usage metrics for a time period (hourly, daily, weekly, or monthly)
get_upcoming_invoiceGet estimated charges for the current billing period
get_invoice_historyGet historical invoices for your organization
get_subscriptionGet subscription details including plan, status, and payment info

API Tokens

ToolDescription
list_tokensList all named API tokens for your organization
create_tokenCreate a new named API token
delete_tokenDelete a named API token

Prompts

The MCP server includes built-in prompts that guide your agent through common multi-step workflows:
PromptDescription
create-dev-instanceSet up a GPU development instance with sensible defaults
deploy-modelDeploy an ML model (supports Ollama, vLLM, and Transformers)
check-costsReview current GPU usage and costs
snapshot-and-teardownSave instance state and clean up
setup-comfyuiSpin up a GPU instance with ComfyUI for AI image generation
setup-jupyterLaunch a Jupyter Lab environment on a GPU instance
fine-tune-modelSet up a GPU instance for fine-tuning with LoRA or full fine-tuning
benchmark-gpuRun a quick GPU benchmark on an instance to verify performance

Example Usage

Once configured, you can ask your AI agent things like:
  • “Spin up an A100 instance with PyTorch”
  • “What GPU types are available and how much do they cost?”
  • “Which GPUs are available right now?”
  • “List my running instances”
  • “Run nvidia-smi on my instance”
  • “Delete instance inst-abc123”
  • “Forward port 8080 on my instance”
  • “Create a snapshot of my instance before I make changes”
  • “Deploy Llama 3 on a GPU”
  • “How much have I spent this month?”
  • “Show my invoice history”
  • “Create an API token for my CI pipeline”

Troubleshooting

Authentication fails or browser doesn’t open: Run /mcp in Claude Code to manually trigger authentication. Make sure you’re logged in to your Thunder Compute account in the browser. “Protected resource does not match” error: The URL in your MCP config must match the server’s configured resource URL exactly. Ensure you’re using https://www.thundercompute.com/mcp. “token has invalid issuer” error: This is a server-side configuration issue. The MCP authentication client must be configured with the correct Stytch Connected Apps domain. Tools not appearing: Restart your AI agent after changing MCP configuration. Most agents only read MCP config on startup.

MCP Directories

Thunder Compute is listed on major MCP directories for easy discovery:
  • Smithery — One-click install for supported clients
  • MCP Registry — The official Model Context Protocol server registry
  • Glama — Auto-indexed from the MCP Registry
  • PulseMCP — Auto-indexed from the MCP Registry
If your MCP client supports browsing directories, search for “Thunder Compute” to find and install the server directly.