Fashio AI

List Fashio generation tools

list_models

Lists every toolId that create_generation accepts and returns {models: [{id, inputShape, promptMode, fallbackPriceDiamonds}]}. Use it first to pick a toolId; use list_fashion_models, list_venues and list_poses instead to choose what goes into images.* and pose. The list is fixed server-side and the same for every key (not per-account), and fallbackPriceDiamonds is approximate: the real price is resolved when the job starts. 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_models"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 8, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does most of it: it discloses that the list is fixed server-side and identical for every key (not per-account), that fallbackPriceDiamonds is approximate and the real price resolves at job start, that the call is free, and that Bearer auth is required with daily request caps (429/401). The only gap is that return value format is spelled out but pagination or response-size behavior is not; for a fixed zero-param list that is a minor omission.

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?

Three dense sentences, front-loaded with purpose and the return shape, then usage routing, then operational caveats — every sentence earns its place. It is near the upper limit of density for a trivial no-arg call, and the inline JSON return snippet adds length an agent may not need given no output schema exists.

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 zero-parameter listing call with no output schema, the description supplies the return shape, the semantic caveat on price accuracy, cost, auth and rate-limit behavior, and sibling routing — everything needed to call it correctly and interpret the result.

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 schema has zero parameters and 100% coverage, so there is nothing for the description to clarify at the argument level; the baseline for a no-param tool applies. The description instead interprets the returned fields (inputShape, promptMode, fallbackPriceDiamonds), which is helpful but belongs to behavioral transparency rather than parameter semantics.

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 every toolId that create_generation accepts") and immediately separates itself from the similarly named siblings by naming list_fashion_models, list_venues and list_poses as the tools for other lookup needs. It also declares the return shape, so an agent knows exactly what it gets.

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 an explicit ordering instruction ("Use it first to pick a toolId") and routes the agent away to three named alternatives when the intent is choosing what goes into images.* or pose. This is the when/when-not/alternatives pattern in full.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Fashio AI tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.