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runpod

RunPod MCP Server

Official
by runpod

create-pod

Provision a GPU or CPU pod on RunPod. Configure image, GPU type, storage, ports, and environment variables for compute workloads.

Instructions

Create a new GPU/CPU pod on RunPod. If the user does not specify an image, recommend the "Runpod Pytorch 2.8.0" image (runpod/pytorch:1.0.2-cu1281-torch280-ubuntu2404) as the default — it has the most up-to-date CUDA and PyTorch versions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoName for the pod
imageNameYesDocker image to use
cloudTypeNoSECURE or COMMUNITY cloud
gpuTypeIdsNoList of acceptable GPU types
gpuCountNoNumber of GPUs
containerDiskInGbNoContainer disk size in GB
volumeInGbNoVolume size in GB
volumeMountPathNoPath to mount the volume
portsNoPorts to expose (e.g., '8888/http', '22/tcp')
envNoEnvironment variables
dataCenterIdsNoList of data centers
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It only offers a default image recommendation, lacking details on permissions, costs, failure modes, or idempotency for this mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose. Efficient but could include more substantive information without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 11 parameters and no output schema, the description is insufficient. It omits details on return values, error handling, default behaviors, and parameter interactions, making it incomplete for a complex creation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers all parameters, providing baseline meaning. The description adds value by recommending a specific default Docker image beyond the schema's description for 'imageName', offering practical guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Create' and resource 'new GPU/CPU pod on RunPod', which is specific and distinct from sibling tools like 'cancel-job' or 'create-endpoint'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives. While sibling tools differ in resource, no direct usage context or exclusions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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