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PyTorch Lightning MCP Server

PyTorch Lightning MCP Server

by prat24
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

lightning.predict

Run inference by providing a model configuration with target and kwargs. Optionally specify trainer settings for prediction.

Instructions

Run prediction/inference with 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.predict"
  }
}

Input Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description bears full burden for behavioral disclosure. It merely states the tool runs prediction/inference, without revealing side effects, state changes, resource requirements, or output characteristics. This is insufficient for an inference tool.

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 a single, efficient sentence containing no filler words. Every word contributes to defining the tool's core purpose.

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?

Given the tool has nested object parameters (model and trainer) and no output schema, the description is too terse. It lacks details on expected configuration formats, defaults, or return behavior, making it incomplete for an agent to use effectively.

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 coverage is 100% (both parameters described in schema). The description adds no extra meaning beyond the schema's descriptions of 'model configuration' and 'trainer configuration.' Baseline 3 is appropriate since schema fulfills basic needs.

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 as running prediction/inference with a PyTorch Lightning model, using a specific verb and resource. It distinguishes well from sibling tools like lightning.train, lightning.test, and lightning.validate, which focus on training, testing, and validation.

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?

The description provides no guidance on when to use this tool versus alternatives such as lightning.test or lightning.validate. It does not mention prerequisites, common scenarios, or exclusions, leaving the agent to infer appropriate use from context.

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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