RunPod MCP Server
Provides a Node.js-based MCP server for interacting with RunPod's API, with tools for managing cloud resources including GPU pods and serverless endpoints.
Facilitates provisioning and management of NVIDIA GPU resources through RunPod, allowing users to create and manage pods with specific NVIDIA GPU types.
Enables deployment and management of PyTorch environments through RunPod, supporting container images like runpod/pytorch for AI/ML workloads.
Supports management of Ubuntu-based containers in RunPod, including deployment of environments like Ubuntu 22.04 with CUDA support.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@RunPod MCP Server list all my pods"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
RunPod MCP Server
This Model Context Protocol (MCP) server enables you to interact with the RunPod REST API through Claude or other MCP-compatible clients.
Features
The server provides tools for managing:
Pods: Create, list, get details, update, start, stop, and delete pods
Endpoints: Create, list, get details, update, and delete serverless endpoints
Templates: Create, list, get details, update, and delete templates
Network Volumes: Create, list, get details, update, and delete network volumes
Container Registry Authentications: Create, list, get details, and delete authentications
Related MCP server: RunPod MCP Server
Setup
Prerequisites
Node.js 18 or higher
A RunPod account and API key
Claude for Desktop or another MCP-compatible client
Installing via Smithery
To install runpod-mcp-ts for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @runpod/runpod-mcp-ts --client claudeInstallation
Clone the repository
Install dependencies:
npm installBuild the server:
npm run build
Configuration
Set your RunPod API key as an environment variable:
# Linux/macOS
export RUNPOD_API_KEY=your_api_key_here
# Windows (Command Prompt)
set RUNPOD_API_KEY=your_api_key_here
# Windows (PowerShell)
$env:RUNPOD_API_KEY="your_api_key_here"You can get your API key from the RunPod console.
Running the Server
Start the server:
npm startSetting up with Claude for Desktop
Open Claude for Desktop
Edit the config file:
~/Library/Application Support/Claude/claude_desktop_config.json(macOS) or%APPDATA%\Claude\claude_desktop_config.json(Windows)Add the server configuration:
{
"mcpServers": {
"runpod": {
"command": "node",
"args": ["/path/to/runpod-mcp-server/build/index.js"],
"env": {
"RUNPOD_API_KEY": "your_api_key_here"
}
}
}
}Make sure to replace the "args": ["/path/to/runpod-mcp-server/build/index.js"] with the path to the build folder in the repository.
Restart Claude for Desktop
Usage Examples
Here are some examples of how to use the server with Claude:
List all pods
Can you list all my RunPod pods?Create a new pod
Create a new RunPod pod with the following specifications:
- Name: test-pod
- Image: runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04
- GPU Type: NVIDIA GeForce RTX 4090
- GPU Count: 1Create a serverless endpoint
Create a RunPod serverless endpoint with the following configuration:
- Name: my-endpoint
- Template ID: 30zmvf89kd
- Minimum workers: 0
- Maximum workers: 3Security Considerations
This server requires your RunPod API key, which grants full access to your RunPod account. For security:
Never share your API key
Be cautious about what operations you perform
Consider setting up a separate API key with limited permissions
Don't use this in a production environment without proper security measures
License
MIT
Available Tools
26 toolscreate-container-registry-authD
Inputs: name (required), password (required), username (required).
create-endpointD
Inputs: templateId (required), name, gpuCount, gpuTypeIds, +4 more.
create-network-volumeD
Inputs: name (required), size (required), dataCenterId (required).
create-podD
Inputs: imageName (required), env, name, ports, +7 more.
create-templateD
Inputs: name (required), imageName (required), env, ports, +7 more.
delete-container-registry-authD
Inputs: containerRegistryAuthId (required).
delete-endpointD
Inputs: endpointId (required).
delete-network-volumeD
Inputs: networkVolumeId (required).
delete-podD
Inputs: podId (required).
delete-templateD
Inputs: templateId (required).
get-container-registry-authD
Inputs: containerRegistryAuthId (required).
get-endpointD
Inputs: endpointId (required), includeWorkers, includeTemplate.
get-network-volumeD
Inputs: networkVolumeId (required).
get-podD
Inputs: podId (required), includeMachine, includeNetworkVolume.
get-templateD
Inputs: templateId (required).
list-container-registry-authsD
Inputs: no named parameters.
list-endpointsD
Inputs: includeWorkers, includeTemplate.
list-network-volumesD
Inputs: no named parameters.
list-podsD
Inputs: name, gpuTypeId, computeType, dataCenterId, +2 more.
list-templatesD
Inputs: no named parameters.
start-podD
Inputs: podId (required).
stop-podD
Inputs: podId (required).
update-endpointD
Inputs: endpointId (required), name, scalerType, workersMax, +3 more.
update-network-volumeD
Inputs: networkVolumeId (required), name, size.
update-podD
Inputs: podId (required), env, name, ports, +4 more.
update-templateD
Inputs: templateId (required), env, name, ports, +2 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 26 tools
All 26 tools have distinct names combining a unique action verb and resource noun, making each tool clearly differentiated from the others without any overlap.
Every tool follows the consistent verb_noun pattern in snake_case, using appropriate verbs like create, delete, get, list, start, stop, and update with the same set of resource nouns.
With 26 tools, the server exceeds the recommended range of 3-15 for a well-scoped set. The number feels heavy for managing five resource types, though the operations are systematic.
Most resources have full CRUD coverage plus start/stop for pods. The only missing operation is update for container-registry-auth, but overall the tool surface covers the core lifecycle actions well.
Maintenance
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