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Trandu1
by Trandu1

render_voiceover

Convert narration text into local MP3 audio files via OpenRouter speech models, with optional voice cloning and custom output paths for voiceover production.

Instructions

Render narration text to a local audio file via OpenRouter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe narration script. Vietnamese diacritics are preserved verbatim.
voiceNoProvider voice id. Leave empty to use the model's default -- Fish Audio documents no preset voice ids, so empty is correct there.
overwriteNoFalse (default) never clobbers an existing file -- a numbered sibling is written instead and returned as audio_path.
output_pathNoAbsolute or relative destination file (e.g. "D:/campaigns/abc/audio/narration.mp3"). Parent directories are created. Leave empty to write into the configured output directory.
instructionsNoTone direction. Only forwarded to providers that document it (currently OpenAI); otherwise reported back in "warnings".
reference_textNoOptional transcript of the reference sample.
response_formatNo"mp3" (default) or "pcm".
reference_audio_pathNoOptional local audio sample for stateless voice cloning on models that support it.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing side effects and behavioral traits. The one-sentence description only states the basic action—rendering text to a file—but does not disclose overwrite behavior, provider-specific quirks, file creation details, or failure modes. The rich behavioral details are buried in parameter descriptions, but the main description fails to summarize them.

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?

The description is a single, focused sentence that states the core purpose without unnecessary words. It is front-loaded and immediately communicates what the tool does. There is no bloat, and the sentence 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?

Given 8 parameters, full schema coverage, and an output schema, the description is not required to explain every parameter. However, it lacks contextual guidance on when to use this tool versus alternatives, and does not hint at the short/long distinction that the sibling names imply. The description is adequate for the basic function but incomplete for selection decisions.

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 100%, so the baseline is 3. The description itself adds no parameter-specific meaning beyond what the schema already provides. However, the schema descriptions are detailed (e.g., overwrite behavior, forward-only for instructions, numbering of siblings), which makes the overall parameter handling strong. Since the description text does not augment the schema, it stays at the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('render') and resource ('narration text to a local audio file') and mentions the transport ('via OpenRouter'). However, it does not explicitly differentiate from the sibling 'render_long_voiceover' other than by name, which leaves some ambiguity about when each is appropriate.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus its siblings (e.g., render_long_voiceover, preview_voice). It does not mention prerequisites, typical scenarios, or alternatives. The agent must infer usage from the name and parameter descriptions, which is insufficient.

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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