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List logo models

list_logo_models
Read-onlyIdempotent

List the image models that can design a logo, each with what one logo costs in coins, plus who pays — payer personal with the coin balance, or payer team with what may be spent from the team's pool. renders_text marks models that reliably spell a brand name — prefer them for marks that carry the name (combination, wordmark, monogram, emblem).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, and the description still adds real substance: the payer dimension (personal coin balance vs team pool) and the renders_text reliability flag, both of which shape downstream decisions.

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

Conciseness4/5

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

Two sentences, front-loaded with what is listed and followed by the practical meaning of the renders_text field. Dense but every clause carries information; no filler or restatement of the name.

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?

With no output schema, the description must carry the return-value burden, and it does: it explains coin cost, payer semantics with balances, and the renders_text marker. For a zero-parameter listing tool with no output schema, nothing essential is missing.

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?

Zero parameters, so the baseline is 4. There is nothing for the description to clarify on inputs, and it does not waste space inventing parameter details.

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?

Specific verb (List) plus a precise resource (image models that can design a logo), and it enumerates the returned attributes (coin cost, payer, renders_text) so an agent knows this is a model catalog, not a list of created logos — distinguishing it from the sibling list_logos.

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

Usage Guidelines3/5

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

The description gives model-selection advice (prefer renders_text models for combination, wordmark, monogram, emblem marks) but never states when to call this tool versus alternatives such as list_logos or create_logo. Usage is only implied by the return content.

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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