Shootika

List models

list_models

Read-only

Lists the AI models (the people shown wearing or carrying the product) this store can use: id, name, body type, skin tone, the poses each can take, its subject and its frames_per_shoot (the frames one shoot with it uses). A model with subject 'mannequin' is a ghost mannequin: the product shown hollow, with no person in it, on the studio styles only; a shoot with it uses 2 frames instead of 1. Use it with list_styles to choose a scene. Returns every model in one result; takes no arguments.

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 observedOct 25, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already cover read-only and closed-world, but the description adds genuinely new behavior: results are returned in one unpaginated result, the call takes no arguments, and ghost-mannequin models consume 2 frames per shoot instead of 1 and apply only on studio styles. That frame-cost rule materially affects downstream planning, though other side effects on shoot creation aren't discussed.

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 fields, then the special-case rule, then usage pairing. The nested parentheticals make it denser than necessary and could be split into a short list, but nearly every clause carries information the agent needs.

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?

There is no output schema, so the description carries the return-value burden and does so by enumerating the fields plus the semantic meaning of 'subject' and 'frames_per_shoot'. With no parameters and read-only annotations, nothing an agent needs in order to call this 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?

Zero parameters, so the baseline is 4; the description also explicitly notes 'takes no arguments', confirming to the agent that no filtering or pagination input exists. No additional parameter detail is possible or needed.

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+resource ('lists the AI models') and immediately defines the domain term as 'the people shown wearing or carrying the product', which distinguishes it from sibling list_* tools. The enumerated return fields (id, name, body type, skin tone, poses, subject, frames_per_shoot) pin down exactly what a model record is.

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

Usage Guidelines4/5

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

'Use it with list_styles to choose a scene' gives explicit pairing guidance, which is the main downstream use case. It does not state an exclusion or contrast the tool against alternatives (e.g. when to prefer list_scenes), so it falls short of a full when/when-not treatment.

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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