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Glama

Generate Voiceover / Narration

generate_voiceover

Convert a script or subject into spoken WAV audio using system TTS or an optional Hugging Face model, returning the file path and exact duration.

Instructions

Use to turn a script into spoken audio (WAV). Offline by default via the system TTS engine (macOS 'say', pico2wave, ffmpeg's flite, or espeak-ng): intelligible but synthetic-sounding, loudness-normalized to -16 LUFS. If no engine exists you get a timing placeholder TONE that is explicitly labelled NOT SPEECH. generative:true uses your Hugging Face TTS model (HF_TTS_MODEL; quota, budget-guarded) for a natural voice. Returns the WAV path and exact duration in ms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoDelivery: 'dramatic' (slower, deeper), 'calm documentary', 'energetic'.
scriptNoExplicit script (overrides subject as the spoken text).
subjectYesThe narration text (or a topic if you also pass script).
generativeNoUSES QUOTA: natural voice via HF_TTS_MODEL.
approveOverBudgetNoOnly after the user agrees: proceed past the budget guard.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses the default engine chain, the loudness normalization target (-16 LUFS), the explicit failure-mode fallback (a timing TONE labelled NOT SPEECH), quota/budget-guarding for the generative path, and the return payload (WAV path plus exact duration in ms). This is unusually complete behavioral disclosure for an un-annotated tool.

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?

Front-loads the purpose and the default mode before the generative option, and every sentence carries information (engine fallback, normalization, budget guard, return values). It is dense with parenthetical lists and runs slightly long, but contains no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no annotations exist, so the description must cover behavior and returns, and it does: it names both the offline and generative paths, the degenerate fallback case, the budget approval flow, and the returned fields. An agent has everything needed to invoke it correctly and interpret the result.

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 description coverage is 100%, so baseline would be 3; the description earns above that by explaining the runtime consequence of 'generative' (HF model, quota, budget guard) and the ordering constraint on 'approveOverBudget' (only after user agreement), which the schema texts state only tersely. 'style' and the subject/script interaction are left to the schema.

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 ('turn a script into spoken audio (WAV)') and immediately disambiguates the two modes of production. An agent can distinguish it from generate_soundtrack/generate_sfx/generate_image without opening any schema, since the output artifact is named explicitly.

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 clear conditional guidance: offline is the default, and 'generative:true' switches to the HF model with quota and budget guard. It also explains the approveOverBudget precondition ('Only after the user agrees'). It does not name sibling alternatives or state when NOT to use this tool, 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.