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HiggsHole

HiggsHole

by szk1234
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_models

List available image and video models to see supported resolutions, durations, aspect ratios, and reference-image slots before generating media.

Instructions

List available image and video models with their capability constraints. Read this before generating: supported resolutions, durations, aspect ratios and reference-image slots differ per model.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
kindNoRestrict the listing to one media kind.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns capability constraints (resolutions, durations, etc.) and that these differ per model. For a read-only listing tool, this is sufficient transparency; no contradictions.

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?

Two sentences with no waste. The first sentence states the purpose, and the second provides critical usage context. Front-loaded and efficient.

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

Completeness4/5

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

Given sibling tools (generate_image, generate_video) and no output schema, the description is nearly complete. It tells the agent to use this before generating and hints at the output. Could explicitly mention return type, but sufficient for this context.

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% for the single optional parameter 'kind'. The schema already describes its role. The description adds context about what the tool returns (capability constraints) but doesn't add new details about the parameter itself. Baseline 3 is appropriate.

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: 'List available image and video models with their capability constraints.' It uses a specific verb (list) and resource (models), and distinguishes itself from sibling tools like generate_image and generate_video by indicating it's a preliminary step.

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 advises 'Read this before generating,' implying use before generate_image or generate_video. It explains that constraints differ per model, giving context for when to use. However, it doesn't explicitly state when not to use or mention alternatives beyond the implied workflow.

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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