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Generate character looks

generate_image

Generate new looks of an existing character from its base image while keeping the same face. Describe only scene, outfit, and lighting; images process in the background and return IDs for clips.

Instructions

Generate one or more new looks (reference images) of an existing character. Same as clipugc images generate. Every look is generated from the character's base image, so the face stays the same person: only describe the new setting, outfit and light in scene, never the person again. Costs 2 credits per shot (confirm with get_credits). Returns the created image ids. Looks generate in the background. Poll list_images with the same character id every 5 to 10 seconds until each new look has status "completed" (or "failed"), then pass the look id to create_clip.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sceneNoScene prompt, max 600 chars (same as --scene).
shotsNoComma-separated shot types: frontal, three_quarter, profile, back (same as --shots). Default "frontal".
templateNoTemplate (same as --template). Omit to let the server pick: scene_recreation when scene is set, else model_digitals.
characterYesCharacter id, as returned by other tools (numbers are fine as strings).
resolutionNoResolution (same as --resolution). Default 2K.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses cost, asynchronous background generation, expected polling cadence, success/failure statuses, and that results are image IDs. These are exactly the behaviors not visible in the annotations or schema, and nothing contradicts the annotation hints.

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?

Five dense sentences with a clear order: purpose, alias, generation constraint, cost, output, and polling workflow. Every sentence contributes information not otherwise available in structured fields, and the key purpose is front-loaded.

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?

Despite having no output schema, the description explains return values (image IDs) and the full lifecycle: trigger, poll for terminal status, and continue into create_clip. It is complete enough for an agent to invoke correctly and integrate with siblings.

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 the baseline is 3, but the description adds meaningful semantics for scene (describe only setting, outfit, light; never the person again) and shots (cost is per shot). It does not over-repeat parameter names and leaves schema definitions to carry enums and defaults.

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?

The description opens with a specific verb and resource: generate new looks/reference images of an existing character. It also differentiates from character creation by emphasizing the character already exists, and the downstream reference to create_clip further positions it in the workflow.

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?

It clearly establishes when to use the tool (produce new looks for an existing character), how to budget (2 credits per shot, confirm with get_credits), and how to follow up (poll list_images, then pass IDs to create_clip). It lacks an explicit when-not-to-use statement naming sibling alternatives, so it doesn't quite reach the top bar.

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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