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list_image_lab_models

Read-only

List Image Lab models, modes, and aspect ratios. Same catalog as /image-lab: ChatGPT Image 2.5, Nano Banana, Nano Banana 2, Nano Banana 2.1, Nano Banana Pro, FLUX 3 Image, FLUX.2 Pro, Ideogram V4.5, MAI Image 2.5, MAI Image 2.5 Pro, Seedream 5 Lite, Seedream 5 Flash, Muse Image, Grok Imagine Image 2.0, Qwen Image 3, Kling Omni 3, and Recraft V4.1 Flash.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoImage Lab catalog (slug, modes, aspect ratios).
defaultModelSlugNoDefault Image Lab model slug.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered and the description adds nothing behavioral on top of it — no caching, no auth requirement, no note that the catalog is static versus dynamic. It does disclose that the result includes modes and aspect ratios alongside models, which is modest added value, but not a behavioral trait beyond the annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is correctly front-loaded in sentence one, but sentence two is a seventeen-item model roster that is volatile data and, with an output schema present, duplicates what the tool returns. That roster consumes the majority of the text without changing how an agent selects or invokes the tool.

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

Completeness3/5

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

For a zero-parameter, read-only catalog tool with an output schema, return values need not be explained, so the definition is functionally callable. What is missing is the surrounding context an agent needs — namely that this catalog feeds model selection in generate_image_lab or quote_image_lab — leaving the tool adequately described but not well placed in the workflow.

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

Parameters4/5

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

The tool takes zero parameters, which is the baseline-4 case. Schema description coverage is 100% and there is nothing for the description to disambiguate, so no penalty is warranted.

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 first sentence gives a specific verb and resource ("List Image Lab models, modes, and aspect ratios") and the name plus resource scope cleanly separates it from list_image_lab_generations, which lists past runs rather than the catalog. The "Same catalog as /image-lab" aside is a routing note, not purpose, and the rest of the text is content enumeration rather than further purpose definition.

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?

There is no statement of when to call this versus alternatives, even though the sibling set contains obvious consumers (generate_image_lab, quote_image_lab, list_image_lab_generations). The only contextual hint is "Same catalog as /image-lab," which implies equivalence but never says the tool should be called to discover valid model identifiers before generating or quoting.

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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