RunPod MCP

RunPod MCP connects an AI agent to RunPod's GPU cloud through the Model Context Protocol, wrapping the RunPod REST API so infrastructure can be inspected and changed from a chat client or coding assistant instead of the web console. The server exposes RunPod's core compute objects. An agent can create, list, describe, start, stop and delete pods, setting GPU type and count, container image, environment variables, exposed ports, container disk size, attached storage and data center. It can manage serverless endpoints along with their autoscaling settings such as minimum and maximum workers, scaler type and idle timeout. It also covers reusable templates that bundle a container configuration, network volumes for storage that persists across pods, and container registry credentials for pulling private images. It fits teams running model training, fine-tuning or inference on rented GPUs who want an assistant to spin up a machine, check what is running, and tear things down when a job finishes. The server can be used as a hosted endpoint with a RunPod sign-in, or run locally with an API key held in the client config. Because the same tools that list a pod can also delete one and start billable capacity, scope the key and keep approval prompts on for write actions.