RunPod API Connector for AI agents.
7 live capabilities
Provision and manage cloud GPU hardware via your agent.
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Why people use RunPod API
RunPod for Cloud GPU Infrastructure Management
With this Connector, that entire workflow disappears. You just tell your agent what you need in plain English. It finds the hardware, spins up the pod, and gives you the status update in one go. You get to stay in your flow while the agent handles the infrastructure.
What Vinkius changes
You get a hands-free way to manage cloud GPU resources through natural language.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Quickly spinning up training hardware
An AI researcher needs an A100 for a Llama-3 run.
- Real-world use case 02
Auditing cloud spend at the end of the day
A DevOps engineer asks the agent to list all pods and stop any that have been idle for over four hours to prevent unnecessary billing.
- Real-world use case 03
Checking production inference routes
An MLOps engineer needs to verify that all serverless endpoints are correctly routing traffic.
Complete set · 7capabilities
The complete RunPod API capability set.
These are the exact actions your AI can choose when you ask it to work with RunPod API.
01—04
4 capabilities in this set.
Part of 7 available through RunPod API.
- 01 Capability
Create pod
Create a new GPU pod with a specific name, hardware type, and Docker image. This is useful for starting new training jobs.
- 02 Capability
Get pod
Fetch the status and details of a specific GPU pod. Use this to check if your training job is still active.
- 03 Capability
List endpoints
See every serverless endpoint currently routing your inference traffic. This helps you audit your production deployments.
- 04 Capability
List gpu types
Check the list of available GPU hardware options in your region. Use this to find the best hardware for your model.
05—07
3 capabilities in this set.
Part of 7 available through RunPod API.
- 05 Capability
List pods
Show every pod in your account, including active and paused ones. This is great for a quick inventory of your resources.
- 06 Capability
Stop pod
Halt a running GPU pod to stop incurring hourly costs. This is your primary capability for managing your cloud budget.
- 07 Capability
List templates
View your saved pod configurations and deployment templates. Use this to quickly see your pre-configured environments.
Set up in minutes
One URL. Then ask RunPod API to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use RunPod API from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it RunPod API, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable RunPod API for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the RunPod API URL.
- Step 03
Save and start
Save the connection and enable RunPod API in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"runpod": {
"url": "https://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using RunPod API
Open Agent mode in chat and ask: "Using RunPod API, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"runpod": {
"url": "https://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using RunPod API
Ask Copilot: "Using RunPod API, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"runpod": {
"url": "https://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using RunPod API
Open Cascade and ask: "Using RunPod API, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"runpod": {
"url": "https://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using RunPod API
Ask Cline: "Using RunPod API, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add runpod --transport http "https://edge.vinkius.com/vk_preview_mqfmppnjNgzd7DnF3DKymtiD5h0tXpOI6WsCj5ji/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using RunPod API
Ask Claude: "Using RunPod API, show me...". 7 tools are ready
Where the request belongs
Work RunPod API can move forward.
This is for the DevOps engineer tired of dashboard fatigue and the AI researcher who needs to spin up compute on the fly without switching tabs.
DevOps Engineer
You use this to audit active pods and stop idle instances to keep cloud costs under control.
AI Researcher
You use this to quickly provision new GPU hardware for training runs or testing different models.
MLOps Engineer
You use this to manage and inspect serverless inference endpoints across your production environment.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsLambda Labs (GPU Cloud)
Manage AI infrastructure via Lambda Labs. launch GPU instances, monitor ML workloads, and manage SSH keys.
Modal (Serverless AI Infrastructure)
Manage serverless compute via Modal. audit active apps, track GPU deployments, and monitor network volumes.
Paperspace
Provision and track powerful GPU workloads via Paperspace. list compute instances, fetch active deployments, trace team projects, and query Gradient environments via AI.
CoreWeave (AI GPU Cloud)
Manage high-performance AI infrastructure on CoreWeave. provision GPU clusters, configure VPCs, and orchestrate inference gateways directly from your AI agent.
Vast.ai (GPU Rental Cloud API)
Rent high-performance GPUs for AI and deep learning. Search marketplace offers, deploy Docker containers, and manage your cloud GPU fleet.
Rancher
Equip your AI to manage Kubernetes environments directly through Rancher, overseeing clusters, namespaces, and active pods.
Bring your own AI
Change the model, client or framework. Keep RunPod API connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about RunPod API.
The practical details behind the request, access and result.
Can RunPod MCP help me save money on my GPU costs?
Yes. You can ask your agent to list all your active pods and stop any that are idle. This prevents you from paying for compute cycles you aren't actually using.
Does RunPod MCP support custom Docker images?
Yes, it does. When you ask your agent to create a new pod, you can specify the exact Docker image you want to use for your workload.
Can I use RunPod MCP to manage serverless inference?
Yes. You can use it to list and audit all your registered serverless endpoints, making it easier to manage production traffic.
What hardware can I see with RunPod MCP?
You can see all the GPU types currently available in your RunPod region, including high-performance options like A100s and H100s.
How do I connect my RunPod account to this Connector?
You just need to generate an API key from your RunPod settings and paste it into the secure connection module in your AI client.
Can the agent create pods automatically?
Yes. Once connected, your agent can provision new pods immediately based on your natural language requests for specific hardware and images.
Can the AI forcefully terminate or delete critical production endpoint fleets on demand?
No. This module safely allows the AI to only pause and manage running instances. Destructive deletion actions (like completely erasing a pod) are intentionally prohibited by the tooling design to protect your critical compute resources from unintended loss.
Can the AI provision large GPU arrays automatically?
Yes. Using the create_pod capability, the AI can query the available hardware models (such as A100 or H100) and immediately launch new Docker clusters based on existing community templates, simplifying complex DevOps scaling actions significantly.
Will the AI know the billing state or the real-time cost of running each endpoint?
No. The current RunPod AI module is concentrated on operational control and system orchestration, such as discovering inactive processes and booting new instances. Deep billing analytics or invoice extraction is not natively integrated in the commands exposed to the AI at this time.
One connection away
Give your agent a direct line to RunPod API.
Connect RunPod API once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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