Fashio AI

List selectable venues/backgrounds

list_venues

Lists active venues/backgrounds from Fashio's catalog in catalog order and returns {venues: [...]}. Pass a venue's id (or an image URL) as images.venue in create_generation (tools that take a venue, e.g. virtual_model, outfit_photo) or create_template_set. Use list_ready_template_sets instead for a complete model + venue + poses look. No paging: only the first limit venues 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_venues"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	How many to return, 1-100 (default 20).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 8, 2026
- addedInput schema / properties / limit / description
```
Added value: +"How many to return, 1-100 (default 20)."
```
First observedNov 8, 2026

TDQS

A4.8/5.0

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: no paging (only first `limit` come back), free of diamond cost, Bearer API key required, per-call daily request cap with 429 and 401 failure modes, and the response envelope. These are operational facts an agent cannot infer 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.

Conciseness4/5

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

Front-loaded with purpose and return shape, then usage, then constraints. It is dense and every sentence carries information, though the parenthetical asides about specific sub-tools make it slightly busier than necessary.

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?

A one-parameter read tool with no output schema, and the description still supplies the return envelope, ordering, truncation behavior, cost, and auth/rate-limit context. Nothing an agent needs to call it correctly 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?

Schema coverage is 100%, so `limit`'s range and default are already documented. The description nonetheless adds meaningful semantics: results are truncated to the first `limit` in catalog order with no paging, which is not obvious from the schema alone.

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?

States a specific verb and resource ("Lists active venues/backgrounds from Fashio's catalog") plus ordering ("in catalog order") and return shape ({venues: [...]}). It explicitly distinguishes itself from list_ready_template_sets, so an agent can pick it without opening the schema.

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?

Names when to use it and how the result is consumed downstream (pass the id as images.venue in create_generation or create_template_set), and names the alternative to use for a full model + venue + poses look. Exclusions are explicit rather than implied.

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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