Skip to main content
Glama
runpod

RunPod MCP Server

Official
by runpod

create-pod

Create a new GPU or CPU pod on Runpod with your chosen Docker image. Supports GPU types, CPU compute, environment variables, and port exposure.

Instructions

Create a new GPU/CPU pod on Runpod. Pass gpuTypeIds for a GPU pod, or computeType:"CPU" for a CPU pod (CPU pods are served by the v1 API for now). 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
envNoEnvironment variables
nameNoName for the pod
portsNoPorts to expose (e.g., '8888/http', '22/tcp')
gpuCountNoNumber of GPUs
cloudTypeNoSECURE or COMMUNITY cloud
imageNameYesDocker image to use
gpuTypeIdsNoList of acceptable GPU types
volumeInGbNoVolume size in GB
computeTypeNoPod type. On v2, required when not passing gpuTypeIds. 'CPU' is served by the v1 API for now (v2 has no CPU pods yet).
dataCenterIdsNoList of data centers
volumeMountPathNoPath to mount the volume
containerDiskInGbNoContainer disk size in GB
Behavior5/5

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

The description adds behavioral context beyond the annotations: it notes that CPU pods use the v1 API, recommends a default image, and distinguishes between pod types. Annotations provide readOnlyHint false and openWorldHint true, which are consistent.

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

Conciseness5/5

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

The description is three sentences, each adding essential information. It is front-loaded with the primary action, then specifies variations and recommendations. No wasted words.

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

Completeness5/5

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

Despite no output schema, the description covers all critical aspects: creation action, parameter usage for GPU vs CPU, image suggestion, and API version note. It is complete for a creation tool with 12 parameters.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the relationship between gpuTypeIds and computeType, and recommends a default image. It does not detail every parameter, but the key semantic distinctions are covered.

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 tool's purpose: creating a new GPU/CPU pod on Runpod. It distinguishes between GPU and CPU pod creation, which is crucial. The tool name itself is unambiguous, and the description adds specific actions.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use which parameters: 'Pass gpuTypeIds for a GPU pod, or computeType:"CPU" for a CPU pod.' It also recommends a default image and notes the CPU pod API version, giving clear usage context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/runpod/runpod-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server