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PyTorch Lightning MCP Server

PyTorch Lightning MCP Server

by prat24
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

lightning.checkpoint

Save, load, or list PyTorch Lightning model checkpoints via a structured API.

Instructions

Manage model checkpoints: save, load, or list.

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": {
      "action": "<action>"
    },
    "name": "lightning.checkpoint"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathNoCheckpoint file path (for save/load).
modelNoModel configuration (for save/load).
actionYesAction to perform.
directoryNoDirectory to list checkpoints from.

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, the description bears full responsibility for behavioral disclosure. It only lists actions (save, load, list) without mentioning side effects, resource implications, permissions, or return behavior. Important details like file creation, overwrite behavior, or required permissions are omitted.

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, concise sentence that immediately conveys the core purpose. It front-loads the verb and resource, with the colon providing a clear separation of action enumeration.

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 4 parameters, nested objects, and no output schema, the description is too minimal. It does not explain when to use each action, what the model object requires, or what return values look like. The multi-action nature demands more context for correct agent invocation.

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?

Input schema has 100% coverage with descriptions for all 4 parameters. The tool description does not add any extra meaning beyond the schema, so the baseline score of 3 is appropriate. No additional context about parameter usage or relationships is provided.

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 managing model checkpoints with three specific actions: save, load, or list. It distinguishes itself from sibling tools like inspect, predict, train, etc., by focusing on checkpoint operations.

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 guidance is provided on when to use this tool versus alternatives. The description only lists actions, but does not specify appropriate contexts, prerequisites, or exclusions.

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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