GPU cloud compute via RunPod — deploy pods, manage GPU instances, start and stop workloads for AI training and inference.
Use with AI AssistantsMCP
Connect Claude, Cursor, or any MCP-compatible client — then call tools directly
① Add This MCP Server
Paste into your AI client config — then all its tools are available instantly.
{
"mcpServers": {
"runpod": {
"url": "https://mcp.aerostack.dev/s/aerostack/mcp-runpod",
"headers": {
"Authorization": "Bearer YOUR_AEROSTACK_TOKEN"
}
}
}
}Replace YOUR_AEROSTACK_TOKEN with your API token from the dashboard.
② Call a Tool
Ask your AI assistant to call a specific tool, or send raw JSON-RPC:
Natural Language Prompt
“Use the _ping tool to verify runpod credentials by calling a lightweight read endpoint. used internally by aerostack to validate credentials”
Using a Workspace?
Add this MCP to your Workspace — your team shares one token, secrets are stored securely, and every AI agent in the workspace can call it without per-user setup.
GPU cloud compute via RunPod — deploy pods, manage GPU instances, start and stop workloads for AI training and inference.
Live endpoint: https://mcp.aerostack.dev/s/aerostack/mcp-runpod
This MCP server gives AI agents access to Runpod via 7 tools. Connect it to any Aerostack workspace and your agents can interact with Runpod directly.
| Tool | Description |
|---|---|
list_gpu_types | List all available GPU types on RunPod with memory, pricing (spot and on-demand), and availability in secure vs community cloud. |
list_pods | List all pods in your RunPod account with their status, image, and machine details. |
get_pod | Get detailed information about a specific RunPod pod including status, GPU count, and logs setting. |
create_pod | Deploy a new GPU pod on RunPod. Creates a secure cloud pod with the specified GPU type and container image. |
stop_pod | Stop a running RunPod pod (pauses billing while preserving the pod configuration and volume). |
resume_pod | Resume a stopped RunPod pod to restart billing and execution. |
terminate_pod | Permanently terminate and delete a RunPod pod. All data not on persistent volume will be lost. |
| Variable | Required | Description |
|---|---|---|
RUNPOD_API_KEY | Yes | Your RUNPOD API KEY from the service's developer settings |
Add the following secrets under Project → Secrets:
RUNPOD_API_KEYOnce added, every AI agent in your workspace can use Runpod tools automatically.
curl -X POST https://mcp.aerostack.dev/s/aerostack/mcp-runpod \
-H 'Content-Type: application/json' \
-H 'X-Mcp-Secret-RUNPOD-API-KEY: your-runpod-api-key' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_gpu_types","arguments":{}}}'
MIT
Live Endpoint
https://mcp.aerostack.dev/s/aerostack/mcp-runpod
Sub-50ms globally · Zero cold start
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Yes. The Runpod MCP includes a create_pod tool that lets Claude create pods in your Runpod account from a plain-English prompt. You can also update and delete pods — no Runpod UI needed.
Yes. The Runpod MCP includes tools to list and retrieve gpu types from your Runpod account. Claude can filter, sort, and summarize gpu types based on your instructions.
Yes. The Runpod MCP uses the open Model Context Protocol standard, so it works in Claude, Cursor, Windsurf, and any other MCP-compatible AI tool. All 7 Runpod tools are available everywhere you connect it — install once on Aerostack.
Yes. Aerostack hosts the Runpod MCP with encrypted credential storage and per-account authentication. Your Runpod credentials are never shared with Claude's conversation — they're used server-side only.