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Create Character Thumbnail

create_character_thumbnail
Destructive

[write, queued, priced] A thumbnail picture (video / post cover) with 1-3 READY characters: thumbnail_prompt is the topic or scene (up to 1100 characters); the picture is framed as an eye-catching thumbnail - the character large with an expressive face, bold colours, free space for a title (no text is drawn; add the title afterwards in the image editor). image_aspect = 16:9 (default, YouTube) | 9:16 | 1:1 | 3:4. Other options as in create_character_image. Poll with get_character_image. Answers image_id and image_uuid.

Input Schema

NameRequiredDescriptionDefault
image_aiNo
image_titleYes
image_aspectNo16:9
character_uuidsYes
character_assetsNo
thumbnail_promptYes
image_environmentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

The bracketed '[write, queued, priced]' adds real signal beyond the annotations: this is an asynchronous job that consumes credits. It also discloses a non-obvious behavioural trait — no text is drawn, so the title must be added in an editor afterwards — plus the polling target. It does not explain the destructiveHint=true cost/irreversibility angle beyond the single word 'priced'.

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?

A single dense paragraph with the operation class front-loaded in brackets and the poll target at the end. Nearly every clause carries information, though the run-on parenthetical about framing is heavier than it needs to be.

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 7-parameter mutation with no output schema, it covers the core prompt semantics, aspect handling, the async polling loop and the returned image_id/image_uuid. The remaining gap is the family of optional nested-object parameters, which are delegated rather than described.

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 description coverage is 0%, so the description must carry the load: it defines thumbnail_prompt (meaning plus a 1100-character limit), image_aspect (all four values plus the 16:9 default) and constrains character_uuids to 1-3 READY characters. However, image_title, image_ai, character_assets and image_environment are left entirely to a pointer at create_character_image, so several parameters remain undocumented here.

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 and resource (create a thumbnail picture for a character) and scopes it precisely: a video/post cover framed with 1-3 ready characters. It differentiates itself from create_character_image by deferring 'other options' there, so an agent can tell the two apart.

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?

Explicitly says to poll with get_character_image and points to create_character_image for the shared option set, which routes the agent correctly through the workflow. It stops short of stating when NOT to use it (e.g. a plain image with no thumbnail framing) — that is only implied by the framing language.

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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