list_modelsList available AI generation models (image, video, edit, upscale engines) by type, with credit costs. These are the AI models that render photoshoots — not the human models/avatars; for those, use list_avatars.
| Name | Required | Description | Default |
|---|---|---|---|
| model_type | Yes | Type: generation, edit, upscale, video, backdrop |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. Description adds that results include credit costs and clarifies the domain (AI models that render photoshoots), but does not cover response shape, pagination, or rate limits. Moderate value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, first is action-oriented and specific. Every phrase earns its place; disambiguation is woven in without bloat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter list tool with read-only annotations, the description fully covers what it lists, the type filter, cost info, and how it differs from list_avatars. No output schema exists, but this is acceptable for a straightforward listing operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single model_type parameter, which already lists allowed values. Description reinforces the 'by type' aspect but adds no new syntax or additional parameter meaning beyond a rough synonym (image/generation).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists AI generation models by type, with credit costs. It explicitly distinguishes from human models/avatars and names list_avatars as an alternative, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: use this for AI models, not human models/avatars, and direct users to list_avatars. Also implies usage by model type, aligning with the required model_type parameter.
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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Most tools target distinct resources and have detailed descriptions, but several closely related families exist: create_credit_checkout_session vs mcp_create_credit_checkout_session, the propose_brief/confirm_brief/update_brief lifecycle, and the many avatar/upload entry points. An agent must read long caveats carefully to avoid selecting the wrong tool.
The vast majority of tool names follow a predictable snake_case verb_noun pattern (list_*, get_*, create_*, update_*, propose_*). The mcp_* prefix group and varied creation verbs (create/upload/save/add/generate) are minor deviations, though mcp_create_credit_checkout_session duplicating create_credit_checkout_session adds some confusion.
With 67 tools, this is an extreme mismatch by the rubric's own 50+ threshold, far beyond the typical 3-15 well-scoped range. Many tools are narrow lifecycle steps such as two-phase local uploads, app-only montage internals, and multiple ArtDirection authoring variants, making the agent-facing surface very heavy.
The core generation, brief, montage, and QA workflows are covered thoroughly with polling and result retrieval. However, notable lifecycle gaps exist: outfits, locations, avatars, and tags mostly have create/list/get but no update or delete, and delete_template is the only delete tool in the entire set.