RunPod MCP: manage GPU cloud infrastructure from your agent
Drive RunPod pods, serverless endpoints, jobs, templates, volumes, registry auth, GPU catalog and billing through structured MCP tool calls β with golden-path playbooks for the multi-step jobs
- What
- Drive RunPod pods, serverless endpoints, jobs, templates, volumes, registry auth, GPU catalog and billing through structured MCP tool calls β with golden-path playbooks for the multi-step jobs
- Cost
- Free
- Needs
- a RunPod account with a payment method and your RunPod API key saved in the secure vault β GPU compute is billed per second, this is paid infrastructure. Nothing to install locally; the agent uses the hosted MCP server (mcp.getrunpod.io) or a local stdio server via npx.
- Install
- Copy the installer prompt below into your Muse β your agent does the rest.
π³ **Paid API required / API payante requise** β RunPod is a paid GPU cloud: compute is billed per second, and you need a RunPod account with a payment method plus an API key. Curated by Skill Harbor β @runpod's official skill for the RunPod MCP server, which exposes RunPod's control plane (the same REST API as runpodctl) as structured tool calls: pods (list, get, create, update, start, stop, restart, delete, stream logs), serverless endpoints (create, update, delete, list workers/releases, QUEUE or LOAD_BALANCER types), jobs (run, runsync, status, stream, cancel, retry, health, purge queue), Hub public catalog deployment, managed pay-per-use public endpoints, templates, network volumes, container-registry auth (including AWS ECR delegations), GPU/CPU catalog and data-center listing, and scoped billing breakdowns. Connection options: hosted MCP server (mcp.getrunpod.io) with your API key as a Bearer header, OAuth ("Sign in with Runpod", MCP-only), or local stdio via npx. The critical guidance: for multi-step jobs, read the verified golden-path sequences (runpod/golden-paths/README.md) before calling tools β tool calls are easy to issue in the wrong order β plus the MCP-vs-runpodctl decision guide (use the CLI for file transfer, SSH, doctor setup, multi-GPU priority lists and CPU endpoints, which MCP cannot create). Honest caveats: real cloud spend happens here β the delete-tool "Unexpected end of JSON input" quirk is a false alarm (204 No Content); the AI workflow is MCP-only, nothing to install locally. Apache-2.0 licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a RunPod account with a payment method and your RunPod API key saved in the secure vault β GPU compute is billed per second, this is paid infrastructure. Nothing to install locally; the agent uses the hosted MCP server (mcp.getrunpod.io) or a local stdio server via npx. Install "RunPod MCP: manage GPU cloud infrastructure from your agent" for me. It gives my agent @runpod's official skill for driving RunPod's control plane through MCP tool calls: pods, serverless endpoints, jobs, Hub deployments, public endpoints, templates, network volumes, container-registry auth, GPU/CPU catalog, data centers, and billing. It also carries the connection guide (hosted MCP with Bearer API key, OAuth, or local stdio), the verified golden-path playbooks for multi-step jobs (image β template β endpoint, pod β volume β serverless, multi-region, autoscaling, monitoring), and the MCP-vs-runpodctl decision guide. Apache-2.0 licensed. Repository: https://github.com/runpod/runpod-plugins-official/blob/main/plugins/runpod/skills/runpod-mcp/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "runpod-mcp". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. connect the MCP server: claude mcp add --transport http runpod -s user https://mcp.getrunpod.io/ --header "Authorization: Bearer $RUNPOD_API_KEY"; then run /mcp to verify it shows Connected). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you β no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the sellerβs own page, usually Stripe.
What does the β next to a creatorβs name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself β always check a build before installing it.