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generate_character_portraits

Generate final character portrait images for an episode after the user approves the quoted cost. Use this to establish character looks before proceeding with character sheets or storyboards.

Instructions

确认后批量出角色定妆图(★仅定妆图):后台异步。定妆图只是单张身份锚——镜头一致性(尤其服装)还需设定图,出完定妆图强烈建议 generate_character_sheets;或直接用 generate_portraits_and_sheets 一步到位。用 get_pipeline_status/get_storyboards 查进度。⚠️ 批量报价确认流程:先调对应的 quote_* 工具,把返回的 estimated_points 原样告诉用户,用户明确同意后,才用返回的 quote_id 调本工具。不要擅自确认。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
quote_idYes来自 quote_character_portraits
episode_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.57

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the disclosure burden and it does so well: it reveals 后台异步, the quote-confirmation prerequisite, and the warning 不要擅自确认. It does not cover permissions, failure modes, or return/polling response details, but the key operational behavior is disclosed.

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?

Every sentence earns its place: scope limitation, sibling alternatives, progress-checking, and the quote-confirmation workflow. The warning is separated and front-loaded after the purpose, making the critical usage constraint easy to spot.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter async tool with no output schema, the description covers purpose, alternatives, prerequisites, and progress monitoring. It omits details about immediate return value and failure/edge-case behavior, but these are not blocking given the async pipeline-status guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%: quote_id is explained in the schema and the description reinforces that it must be the quote_id returned after user consent. However, episode_id has no semantic explanation in either the schema or the description, so one of two parameters remains under-documented.

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 states a specific action and resource: 批量出角色定妆图 and emphasizes ★仅定妆图, distinguishing it from generate_character_sheets and generate_portraits_and_sheets. It makes the tool's scope immediately identifiable without needing to inspect sibling schemas.

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?

It explicitly says to call quote_* first, relay estimated_points to the user, wait for explicit consent, then call with the returned quote_id. It also directs users to generate_character_sheets or generate_portraits_and_sheets when sheet assets are needed and to get_pipeline_status/get_storyboards for progress.

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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