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list_models

Read-onlyIdempotent

Check which ComfyUI model files are installed for a chosen type, filter by name, or retrieve a LoRA's embedded base model and trigger words for workflow setup.

Instructions

Model files installed on the instance. folder picks the model type: checkpoints, loras, vae, diffusion_models, text_encoders, upscale_models, controlnet, embeddings, ... (unknown folder -> the full available list). search filters filenames (case-insensitive substring). metadata_for (a .safetensors filename from this folder) returns its embedded training metadata instead - base model + top trigger tags, key for using a LoRA.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
folderNocheckpoints
searchNo
metadata_forNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed4 schema fields changedv0.15.1
    • removedInput schema / properties / folder / title
      Removed value: -"Folder"
    • addedInput schema / properties / metadata_for
      Added value: +{
      +  "default": "",
      +  "type": "string"
      +}
    • removedInput schema / properties / search / title
      Removed value: -"Search"
    • removedInput schema / title
      Removed value: -"list_modelsArguments"
  2. First observedv0.2.0

TDQS

A4.6/5.0
Behavior5/5

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

The annotations already signal read-only, idempotent, and open-world behavior, so the description adds real value by explaining conditional behavior: unknown folder values produce the full list, search is case-insensitive substring matching, and metadata_for switches from listing to embedded training metadata. These are precisely the behaviors an agent would not infer from the schema or annotations.

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 compact and front-loaded with the primary purpose. Each sentence earns its place, and even the longer metadata_for sentence is dense with useful, non-redundant detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description does not need to explain return shapes. It covers default-oriented behavior, edge cases like unknown folders, search matching, and the special metadata_for mode, making the tool safe and effective to invoke correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description is the only source of parameter meaning. It fully compensates by explaining folder with examples and fallback behavior, clarifying search semantics, and defining metadata_for's special behavior.

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 identifies the resource as 'model files installed on the instance' and makes the tool's scope obvious relative to workflow/template siblings. However, it lacks an explicit verb such as 'lists' or 'returns' in the opening sentence, relying partly on the tool name to establish the action.

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

Usage Guidelines4/5

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

The description gives clear operational guidance: folder selects the model category, search filters filenames, and metadata_for fetches metadata instead. It does not explicitly name alternatives or exclusions among the sibling tools, but the context strongly implies when this read-only model-listing tool is appropriate.

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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