Fashio AI

List selectable AI person models

list_fashion_models

Lists active AI person models from Fashio's catalog in catalog order and returns {fashionModels: [...], facets} (facets = the distinct gender/race/bodyType/ageRange values across the whole catalog). Pass a model's id (or an image URL) as images.fashion_model in create_generation or create_template_set. Use list_ready_template_sets or list_template_sets instead when you want a model already paired with a venue and poses. No paging: only the first limit models come back. Free (no diamonds). Needs a valid Fashio API key (Bearer); every call counts toward the key's daily request cap (429 when reached, 401 for a missing or revoked key).

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_fashion_models"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	How many to return, 1-100 (default 20).
`gender`	No	Only models of this gender: male \| female. Omit for both.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 8, 2026
- addedInput schema / properties / gender / description
```
Added value: +"Only models of this gender: male | female. Omit for both."
```
- addedInput schema / properties / limit / description
```
Added value: +"How many to return, 1-100 (default 20)."
```
First observedNov 8, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses the no-paging behavior ('only the first `limit` models come back'), the cost model ('Free (no diamonds)'), Bearer auth requirements, per-key daily request cap, and the 429/401 failure modes. It also explains the shape of the returned facets object, which is behavior an agent cannot get from the schema.

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?

Front-loads the purpose and return shape, then layers downstream usage, sibling alternative, paging caveat, cost, and auth in descending order of importance. Every sentence adds distinct operational information; there is no filler.

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?

For a two-parameter read tool with no annotations and no output schema, the description covers everything an agent needs: what comes back, how facets relate to the full catalog vs. the truncated list, how to consume the ids, when to pick a sibling, cost, and error conditions.

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?

Schema description coverage is 100%, so the baseline is 3; the description adds real meaning on top by clarifying that `limit` is a hard truncation with no paging offset rather than a page size. It says nothing extra about the gender filter beyond the enum, which is already fully documented in the schema.

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?

Opens with a specific verb and resource: 'Lists active AI person models from Fashio's catalog in catalog order', and names the return envelope. It distinguishes itself from list_ready_template_sets and list_template_sets, but never mentions the sibling list_models, leaving that one overlap unresolved.

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

Usage Guidelines5/5

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

Gives explicit when-to-use-alternatives guidance ('Use list_ready_template_sets or list_template_sets instead when you want a model already paired with a venue and poses') and tells the agent what to do with the result (pass the id or an image URL as images.fashion_model in create_generation/create_template_set). Nothing is left to inference.

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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