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ParalonCloud Rentals

@paraloncloudRun by its publisher: served from their own domain, namespace checked by the registry.MCP server · External

Rent GPUs from ParalonCloud: browse, start, poll, and stop GPU rentals over the rental API.

Cloud infrastructurev1.0.3MITJavaScript
Freeno charge per call

Runs on your machine from its package; the steps are below.

What it does

  1. Reads
  2. Writes
  3. High impact

At most: moves money, runs code or commands, deletes data, or sends messages for you.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

npm@paraloncloud/mcp-rentalsv1.0.3

Run

npx -y @paraloncloud/mcp-rentals

MCP client config

{
  "mcpServers": {
    "paraloncloud-rentals": {
      "command": "npx",
      "args": [
        "-y",
        "@paraloncloud/mcp-rentals"
      ]
    }
  }
}
README2 min read

ParalonCloud Rentals — MCP server

Rent GPUs from inside your AI agent. This is an MCP server for the ParalonCloud Rental API: it lets Claude Code, Claude Desktop, Cursor, or any MCP client browse GPUs, start a rental, get its connection URL, and stop it — using natural language.

One key for everything: the same prlc_ key powers ParalonCloud's OpenAI-compatible inference API. Build an agent that calls a model and rents the GPU to run the heavy job.

Tools

ToolWhat it doesCost
list_gpusList rentable GPUs with price, VRAM, compute capability, countryfree
get_balanceYour credit balancefree
create_rentalStart a Jupyter rental (async → poll)spends credits
get_rentalStatus + connection URL once runningfree
list_rentalsYour active rentals (status: "all" for history)free
destroy_rentalStop a rental and stop billing—

Setup

1. Get a key with the rental scope

  1. Create an API key in the Console.
  2. Turn on the GPU Rentals scope for that key (rentals are opt-in).
  3. Optionally set Max rentals running at once as a safety cap.

Use a dedicated key for the agent, not your production key.

2. Add it to your MCP client

The client passes your key via the PARALON_API_KEY env var — you never edit the server.

Claude Desktop — claude_desktop_config.json:

json
{
  "mcpServers": {
    "paraloncloud-rentals": {
      "command": "npx",
      "args": ["-y", "@paraloncloud/mcp-rentals"],
      "env": {
        "PARALON_API_KEY": "prlc_your_key_here"
      }
    }
  }
}

Claude Code — one command:

bash
claude mcp add paraloncloud-rentals \
  --env PARALON_API_KEY=prlc_your_key_here \
  -- npx -y @paraloncloud/mcp-rentals

Cursor — .cursor/mcp.json (same shape as Claude Desktop above).

Optional env: PARALON_BASE_URL (defaults to https://paraloncloud.com/api/v1).

3. Try it

"List the cheapest GPUs I can rent, then start a 2-hour Jupyter rental on one with at least 24GB of VRAM."

The agent calls list_gpus, picks a node, and calls create_rental with hours: 2. It then polls get_rental for the Jupyter URL. Say "stop it" and it calls destroy_rental.

Safety

  • create_rental and destroy_rental change what you're billed — your MCP client will ask you to approve them (Claude Code/Desktop confirm tool calls by default). Keep that on.
  • create_rental auto-generates an idempotency key, so a retried call never starts a second GPU.
  • Pass hours so a rental auto-stops even if the agent forgets to.
  • The key's max_active_rentals limit (set in the Console) caps concurrency regardless.

Run locally (dev)

bash
PARALON_API_KEY=prlc_your_key_here node server.js

MIT

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