paraloncloud-rentals
OfficialAllows renting GPUs and launching Jupyter notebooks for interactive computing.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@paraloncloud-rentalsList available GPUs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Tool | What it does | Cost |
| List rentable GPUs with price, VRAM, compute capability, country | free |
| Your credit balance | free |
| Start a Jupyter rental (async → poll) | spends credits |
| Status + connection URL once running | free |
| Your active rentals ( | free |
| Stop a rental and stop billing | — |
Related MCP server: Latitude.sh MCP Server
Setup
1. Get a key with the rental scope
Create an API key in the Console.
Turn on the GPU Rentals scope for that key (rentals are opt-in).
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:
{
"mcpServers": {
"paraloncloud-rentals": {
"command": "npx",
"args": ["-y", "@paraloncloud/mcp-rentals"],
"env": {
"PARALON_API_KEY": "prlc_your_key_here"
}
}
}
}Claude Code — one command:
claude mcp add paraloncloud-rentals \
--env PARALON_API_KEY=prlc_your_key_here \
-- npx -y @paraloncloud/mcp-rentalsCursor — .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_rentalanddestroy_rentalchange 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_rentalauto-generates an idempotency key, so a retried call never starts a second GPU.Pass
hoursso a rental auto-stops even if the agent forgets to.The key's
max_active_rentalslimit (set in the Console) caps concurrency regardless.
Run locally (dev)
PARALON_API_KEY=prlc_your_key_here node server.jsLinks
Rental API docs: https://paraloncloud.com/docs/rental-api
Console (create keys): https://paraloncloud.com/console
Inference API: https://paraloncloud.com/docs/inference-api
MIT
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