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PyTorch Lightning MCP Server

PyTorch Lightning MCP Server

by prat24
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

lightning.validate

Validate a PyTorch Lightning model with given model configuration and optional trainer settings. Ensure model correctness before full training.

Instructions

Validate a PyTorch Lightning model.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "lightning.validate"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel configuration (_target_ + kwargs).
trainerNoTrainer configuration.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

C2.9/5.0
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 does not disclose what validation entails (e.g., runs a forward pass on validation data, computes metrics, requires a model and optional trainer) or any side effects (e.g., no model updates, read-only). The agent is left guessing about behavior.

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?

The description is extremely concise at one sentence, which is beneficial for front-loading. However, it sacrifices completeness for brevity; a few more sentences could improve clarity without being verbose. No structural issues.

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 nested objects, no output schema, and no annotations, the description should compensate by explaining what validation does, what the parameters accept in terms of structure (e.g., model config as _target_ + kwargs), and what the tool returns. Currently it is too minimal for an AI agent to use correctly.

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

Parameters3/5

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

Schema description coverage is 100% (both 'model' and 'trainer' have descriptions in the schema), so the description adds no additional meaning beyond 'Model configuration' and 'Trainer configuration'. Baseline 3 is appropriate as the schema already handles parameter semantics adequately.

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

Purpose4/5

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

The description clearly states the verb 'Validate' and the resource 'a PyTorch Lightning model', distinguishing it from sibling tools like lightning.train and lightning.predict. However, it could be more specific by mentioning that validation runs on a validation dataset or computes performance metrics.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. The description does not specify context like 'use after training to evaluate model performance on validation data' or mention prerequisites, making it difficult for an AI agent to choose correctly among siblings.

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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