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by io.github.molkex
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generate_character_expression_sheet

Generate a single image showing the character's FACE with several different expressions in one row, labeled — an "expression sheet". Same job type and same history as generate_character_turnaround — see its docstring for the full story: the 2026-07-11 finding (plain Android-bearer image path is inconsistent for multi-view prompts, the real web composer was reliably consistent) is why this tool exists, but as of 2026-07-26 it no longer drives that composer — pure HTTP with image_inputs=[portrait_media_id] instead (no browser). Multi-view consistency on THIS new path has not been re-verified live yet; the recommendation to keep a detailed physical description saved on the entity carries over from the old finding but hasn't been separately re-tested against the new mechanism either.

account: farm account that owns this entity_id's project.
expressions: list of expression labels, e.g. ["нейтральное выражение",
"удивление", "лёгкая улыбка", "смех"] (default if omitted). Keep to 3-5 for a
readable single-row layout.
character_slot_index: which slot to write into (0 = portrait, default — this
overwrites the existing portrait image with the expression sheet, matching how
this was used when reproducing the feature; 1 = body slot also works).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountYes
entity_idYes
expressionsNo
character_slot_indexNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior1/5

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

The description is transparent about the new unverified HTTP path, the lack of browser involvement, and the fact that default character_slot_index=0 overwrites the existing portrait image. However, this contradicts the destructiveHint=false annotation, since overwriting an existing portrait is a destructive side effect; per the rubric this forces a score of 1.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded, and parameter documentation is separated at the end. The historical/mechanism digression is long and somewhat run-on, making the description more verbose than needed for selection and invocation.

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

Completeness3/5

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

The description covers purpose, mechanism, parameter semantics for most fields, and the overwrite side effect, so an agent can likely invoke it correctly. It leaves gaps around the required entity_id, the actual return/job behavior, and relies on reading generate_character_turnaround's docstring for the full story.

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?

With 0% schema description coverage, the description compensates for account, expressions, and character_slot_index by explaining ownership, label format, recommended count, defaults, and overwrite behavior. It does not explain the required entity_id property at all, and 'default if omitted' for expressions does not state the actual default list.

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 opening sentence states a specific verb and resource: generating a single labeled image of the character's face with several expressions in one row. This clearly differentiates it from siblings like generate_character_turnaround and generate_character_image.

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

Usage Guidelines3/5

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

The description implies the expression-sheet use case and says it shares the job type and history with generate_character_turnaround, but it never explicitly states when to use this tool rather than generate_character_turnaround or generate_character_image. It also omits exclusions or prerequisites, instead deferring to the sibling's docstring.

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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