Cloudgpu MCP Server

CloudGPU MCP

CloudGPU MCP server: let an AI agent rent a GPU by the minute, run a job, bring the results back and shut the GPU down. The same API key and balance also cover CloudGPU's OpenAI-compatible model API, so an agent can call a hosted model without renting anything.

"Fine-tune a 7B base model with LoRA on data/train.jsonl and put the adapter in ./outputs."

The agent checks live prices and quotes you, rents the GPU once you say yes, uploads your code and data, starts training in the background, watches the logs, downloads the adapter and destroys the instance. You get the files and the final cost.

It is a local (stdio) MCP server. It works with any MCP client that can start one: Claude Code, Cursor, Cline, opencode, goose, VS Code, Windsurf and others.

Install

1. Create an API key at cloudgpu.app/api and add a prepaid balance (card or USDT).
2. Add the server to your client. Node.js 18 or newer is required. The key goes in the environment variable CLOUDGPU_API_KEY.

claude mcp add cloudgpu --env CLOUDGPU_API_KEY=cgw-sk-... -- npx -y cloudgpu-mcp

{
  "mcpServers": {
    "cloudgpu": {
      "command": "npx",
      "args": ["-y", "cloudgpu-mcp"],
      "env": { "CLOUDGPU_API_KEY": "cgw-sk-..." }
    }
  }
}

{
  "mcpServers": {
    "cloudgpu": {
      "command": "npx",
      "args": ["-y", "cloudgpu-mcp"],
      "env": { "CLOUDGPU_API_KEY": "cgw-sk-..." }
    }
  }
}

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "cloudgpu": {
      "type": "local",
      "command": ["npx", "-y", "cloudgpu-mcp"],
      "environment": { "CLOUDGPU_API_KEY": "cgw-sk-..." },
      "enabled": true
    }
  }
}

extensions:
  cloudgpu:
    name: cloudgpu
    type: stdio
    cmd: npx
    args: ["-y", "cloudgpu-mcp"]
    envs:
      CLOUDGPU_API_KEY: "cgw-sk-..."
    enabled: true
    timeout: 300

1. Optional but recommended: copy rules/cloudgpu.md into your project as AGENTS.md, .cursor/rules/cloudgpu.md or .clinerules/cloudgpu.md. It tells the agent to quote before renting, download before shutting down, and never leave a GPU running. The server also exposes the same text as the resource cloudgpu://rules.

To check the install, ask your agent: "List the GPUs available, then estimate the cost for a 24 GB GPU for one hour. Do not create anything." You should see list_gpus and estimate_cost calls and a quote, and nothing is rented.

Agent Skill

The same guidance is available as two Agent Skills:

• skills/cloudgpu/SKILL.md: renting and using a GPU. Claude Code loads it on demand when you ask for a GPU job. It expects the cloudgpu MCP tools to be installed (see above).

• skills/cloudgpu-model-api/SKILL.md: calling the OpenAI-compatible model API at https://cloudgpu.app/v1 without renting a GPU. It works with the MCP tools or with just a CLOUDGPU_API_KEY.

• Claude plugin: both skills are included automatically when you install the CloudGPU Claude plugin from this repo's marketplace:

  /plugin marketplace add mrhy7176/cloudgpu-mcp
  /plugin install cloudgpu@cloudgpu
  

• Claude Code, personal install: copy skills/cloudgpu/ and skills/cloudgpu-model-api/ to ~/.claude/skills/.

• Claude Code, project install: copy skills/cloudgpu/ and skills/cloudgpu-model-api/ to .claude/skills/ in your project.

• Cursor, Codex, Cline, Amp and other agents that read Agent Skills: install both skills with the skills CLI:

  npx skills add mrhy7176/cloudgpu-mcp
  

Tools

The server also provides a train_on_cloudgpu prompt.

Guardrails

• A quote first. create_instance only takes a quote_id from estimate_cost and user_confirmed=true.
• Server-side limits. Every instance is created with max_spend_usd, max_hours and an idle limit (60 minutes by default). CloudGPU's servers check them and destroy the instance when one is hit, even if your client has disconnected or your laptop is asleep.
• Your own ceiling. CLOUDGPU_MAX_SPEND_USD and CLOUDGPU_MAX_HOURS cap what the agent may set.
• Results before shutdown. After commands have run, stop_instance refuses until something has been downloaded, unless the user confirms force=true.
• Secrets stay out of the chat. The instance's SSH login is used inside this process and never returned to the model. Tool output is scrubbed of the API key and login details. Uploading .ssh, .env, key files or your whole home directory needs allow_sensitive=true.
• The instance stays manageable. Commands that change the root password, SSH configuration, firewall or the platform's monitoring agent are refused. CloudGPU shuts down instances that it can no longer monitor.
• Jobs are not started twice. run_job sends an Idempotency-Key. Resending the same job returns the same job instead of starting a second GPU, and the server refuses the same key with different parameters.
• Keys with a budget. A key from connect_account has a total spending limit, a number of GPUs it may run at once and an expiry date, all enforced by CloudGPU's servers. Revoke it at https://cloudgpu.app/api and it can no longer start anything.
• Read-only mode. CLOUDGPU_READ_ONLY=1 registers only the look-up tools.

Configuration

Model API on the same balance

The key also works with the OpenAI-compatible API at https://cloudgpu.app/v1. Call list_models (or GET /v1/models) to see the available model IDs. Model prices are on cloudgpu.app/api.

Without MCP

The server wraps CloudGPU's public REST API, which any agent or script can call with the same key:

curl https://cloudgpu.app/v1/gpu/offers -H "Authorization: Bearer $CLOUDGPU_API_KEY"

Command-line tool

The package also installs a cloudgpu command for using the same API from a terminal (Node.js 18.3 or newer, no extra dependencies):

npm install -g cloudgpu-mcp      # or run it once with: npx -p cloudgpu-mcp cloudgpu --help
cloudgpu login                   # saves your key to ~/.config/cloudgpu/config.json (mode 0600)
cloudgpu offers                  # GPU models with live price and availability
cloudgpu up --gpu "RTX 4090" --template comfyui --max-minutes 120 --max-spend 5 --wait
cloudgpu ssh <id>                # opens your system ssh to the instance
cloudgpu down <id>               # destroys it and stops billing (asks first; --yes to skip)

CLOUDGPU_API_KEY takes precedence over the saved key, and CLOUDGPU_BASE_URL overrides the API host. Read commands accept --json; SSH passwords are left out of JSON unless you pass --show-password. Every command has --help. Errors print the API's message and exit non-zero (2 for usage errors).

Links

• MCP docs: https://cloudgpu.app/docs/mcp
• GPU pricing: https://cloudgpu.app/pricing
• Model API and prices: https://cloudgpu.app/api
• API reference: https://cloudgpu.app/api/docs · OpenAPI: https://cloudgpu.app/openapi.json · For LLMs: https://cloudgpu.app/llms.txt

CloudGPU here means cloudgpu.app. Prepaid balance, billed per minute; when the balance runs out, running instances are destroyed and their disks erased, so keep enough balance for the job and download results as you go.

Development

npm install
npm test
node src/index.js   # stdio server; set CLOUDGPU_API_KEY first
node src/cli.js --help

License

MIT, see LICENSE.