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Cast a voice from a face

cast_voice

Read a portrait already in the account and return the voices that suit it, best first. Returns ids to pass to generate_voiceover; spends no credits and makes no audio.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
upload_idNo
generation_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNo
modelYes
messageYes
shortlistYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations only supply negative hints (readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=false), so the description's disclosure that it consumes an existing account asset, returns a ranked list, spends no credits, and produces no audio is genuinely additive. The word 'Read' sits slightly uncomfortably with readOnlyHint=false, but the description never claims the tool is non-mutating, and its concrete claims (no credits, no audio) are consistent with destructiveHint=false. It does not say whether the ranking is deterministic or recomputed per call, which matters given idempotentHint=false.

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?

Two short sentences, zero padding, front-loaded with the action and output, with the cost/behavior caveat in second position. Every clause earns its place.

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?

An output schema exists, so return-shape explanation is not required, and the handoff sentence covers the essential workflow. However, with output schema present the definition still leaves the required 'model' parameter and the upload_id/generation_id choice unexplained, which is a real gap for a 3-parameter tool.

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

Parameters2/5

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

Schema description coverage is 0% and there are 3 parameters, so the description carries the full burden — and it does not name or explain any of them. 'A portrait already in the account' vaguely gestures at upload_id/generation_id but never distinguishes the two input paths, and the required 'model' enum (six TTS engines) is completely unaddressed, leaving the agent no basis for choosing one.

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?

Specific verb + resource ('Read a portrait ... return the voices that suit it, best first') with a clear statement of the output ordering and the downstream consumer (generate_voiceover). An agent can tell this apart from generate_voiceover, change_video_voice, and list_models without opening any schema.

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?

The description makes the usage context explicit by naming the follow-on tool: the returned ids 'pass to generate_voiceover'. It also sets boundaries in a negative sense ('spends no credits and makes no audio'), which tells the agent this is the cheap selection step rather than the rendering step. It does not state exclusions or prerequisites for the alternative portrait sources (upload_id vs generation_id).

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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