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自分の画像からアバターを作る

create_avatar

正面・明るい・口を閉じた 1 人の写真 (PNG/JPEG、6MB まで) からアバターを作ります。生成に 2〜3 分かかるので get_avatar で ready になるのを待ってください。メール確認済みのテナントでのみ作れます。プランごとに作れる数に上限があります。 (api_key が必要: 管理画面で発行したキー)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes一覧に出す名前 (60 文字まで)
api_keyYesAPI キー (sk_live_…)
voice_idNo既定の声 (省略で言語 × 性別の既定)
image_urlNo画像の https の URL (image_base64 の代わり)
image_base64No画像の base64 (data URL 可)
voice_genderNo声の性別 (既定 female)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / image_url / description
      Previous value: -"画像の URL (image_base64 の代わり)"New value: +"画像の https の URL (image_base64 の代わり)"
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already flag readOnlyHint=false and openWorldHint=true, so the description earns credit for adding the non-obvious traits: asynchronous generation with a 2-3 minute latency and the need to poll get_avatar, plus tenant verification and quota limits. It stops short of describing failure modes or what happens when the quota is exhausted.

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?

Four tight clauses, each with distinct value: input requirements, async latency and polling instruction, eligibility, and quota/auth note. Front-loaded with the actionable input spec, no filler.

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 creation tool with no output schema, the description covers the essential gaps: input constraints, async behavior and how to observe completion, prerequisites, and auth requirements. It omits what a successful response returns, but polling get_avatar covers the observable outcome, so nothing critical is missing.

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 real constraints beyond the schema: the image must be a single person, front-facing, bright, mouth closed, and under 6MB. It does not clarify the image_url vs image_base64 choice or the voice parameters, but it adds genuine meaning for the critical image input.

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 (avatar creation) plus the exact input medium and constraints (one person, front-facing, bright, mouth closed, PNG/JPEG up to 6MB). This clearly separates it from siblings like list_avatars, get_avatar, and delete_avatar since it is the only tool that originates an avatar from an uploaded photo.

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 routes the agent to the follow-up tool ('wait for ready via get_avatar') and states two eligibility preconditions: email-verified tenant and per-plan creation quota. It does not state when not to use it versus alternatives, but the context it gives is concrete and actionable.

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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