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Multi-voice audio (podcast / dialogue)

multi_voice_to_speech

Render ordered segments, each with its own voice, into ONE merged MP3. Use for podcasts, dialogues, interviews, audio dramas or explainer videos with several speakers when there are no fixed timestamps. Consumes credits.

Input Schema

NameRequiredDescriptionDefault
segmentsYes
wait_secondsNoSeconds the server waits for the audio before returning. If the job is still PENDING, call get_job_status later with the returned job_id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations cover the safety profile (non-read-only, non-destructive, non-idempotent, closed-world), so the bar is lower. The description still adds value the annotations don't: 'Consumes credits' (cost) and the merged-MP3 return format, which matters because there is no output schema. It omits any mention of the async polling flow, which lives only in the wait_seconds schema field.

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 sentences with no filler: the output shape and segment semantics come first, then the use cases and the selection condition. Nothing is repeated from the title or schema.

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 no-output-schema generation tool, it covers purpose, cost, use cases, and return format, and the polling behaviour is carried by the wait_seconds schema description. It leaves segment-count limits and the job_id/get_job_status round-trip unmentioned in prose, which is a minor gap rather than a blocker.

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?

With 50% schema coverage, the description compensates for the undescribed top-level 'segments' parameter by disclosing that segments are ordered and each carries its own voice — real semantic content not present in the schema. It adds nothing about wait_seconds, though the schema already documents that field and its polling fallback.

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 concrete verb and resource ('Render ordered segments ... into ONE merged MP3') and adds the differentiator 'each with its own voice', which separates it from single-voice text_to_speech and timestamp-driven srt_to_speech without needing to open either 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?

Gives explicit use contexts ('podcasts, dialogues, interviews, audio dramas or explainer videos with several speakers') plus a selection condition ('when there are no fixed timestamps'), which implicitly routes timestamped work to srt_to_speech. It never names an alternative tool or states when not to use it, so it stops short of a 5.

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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