PyTorch Lightning MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| lightning.trainC | Train a PyTorch Lightning model with explicit configuration. |
| lightning.inspectC | Inspect models or runtime environment. |
| lightning.validateC | Validate a PyTorch Lightning model. |
| lightning.testC | Test a PyTorch Lightning model. |
| lightning.predictB | Run prediction/inference with a PyTorch Lightning model. |
| lightning.checkpointB | Manage model checkpoints: save, load, or list. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool targets a distinct stage of the PyTorch Lightning model lifecycle (train, validate, test, predict, checkpoint, inspect), with no overlap in functionality.
All tools follow a consistent 'lightning.<action>' pattern (e.g., lightning.train, lightning.test), making it easy to infer purpose from name.
6 tools cover the essential operations for a PyTorch Lightning workflow without being excessive or insufficient.
The set covers the main lifecycle stages, though advanced features like hyperparameter tuning or model export are absent; still satisfactory for core usage.