Skip to main content
Glama

Vidova

by ai.vidova
OverviewDiscussionsAdmin

Generate speech

generate_speech

Generate a voiceover: pick a voice that fits the character. Standard voice: Kore. The text is read word for word in any language it is written in. style directs the delivery in plain words; speakers turns "Name: line" text into a dialogue with a voice per speaker. Load builtin-voiceover first: it lists the tags for laughs, sighs, whispers, shouts and pauses, and a tag not on that list is dropped. It answers at once with a Job ID and the media is made in the background: start every generation you need, keep working, then call wait_for_jobs with all their Job IDs to get each Asset ID for patch_film. transcribe_asset gives its word times. Uses credits by the character: 1,000 characters use 7 credits. Put one voiceover or one whole dialogue in one call rather than splitting it into many short lines.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
styleYesHow to say the whole take, in plain words: "a terrified, urgent whisper", "a deep, slow nature-documentary narrator", "a football commentator who screams the goal". It is never spoken. Give every take one; null is a flat read.
voiceYesVoice for the call, or for lines without a speaker name in a dialogue. Female: Kore (firm), Zephyr (bright), Leda (youthful), Aoede (breezy), Callirrhoe (easy-going), Autonoe (bright), Despina (smooth), Erinome (clear), Laomedeia (upbeat), Achernar (soft), Gacrux (mature), Pulcherrima (forward), Vindemiatrix (gentle), Sulafat (warm). Male: Charon (informative), Puck (upbeat), Fenrir (excitable), Orus (firm), Enceladus (breathy), Iapetus (clear), Umbriel (easy-going), Algieba (smooth), Algenib (gravelly), Rasalgethi (informative), Alnilam (firm), Schedar (even), Achird (friendly), Zubenelgenubi (casual), Sadachbia (lively), Sadaltager (knowledgeable).
speakersYesFor a dialogue: each speaker's name and voice, with every line of text written as "Name: words". Each turn is spoken in its speaker's voice and the turns are joined into one audio asset. null for one voice.
projectIdYesThe project this call runs on without changing the open project. null (or leaving it out) uses the open project. wait_for_jobs takes the projectId of the calls that started the jobs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only cover safety/idempotency flags, but the description adds the async job model (answers at once with a Job ID, media made in background), credit cost (1,000 characters = 7 credits), and the silent-drop behavior for unrecognized tags. These are behavioral traits the annotations do not convey.

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?

Dense but front-loaded: purpose, then workflow, then prerequisites, then cost. Every clause carries operational information, though the run-on single paragraph could be split for easier scanning.

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?

For a 5-required-param generator with no output schema, the description covers the full lifecycle: prerequisite skill, return value (Job ID), follow-up (wait_for_jobs), and handoff (patch_film). Nothing needed to invoke it correctly 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 80% (baseline 3), and the description still adds value: it clarifies text is read word-for-word in any written language, gives the default-style voice (Kore), and explains that style governs delivery in plain words while speakers converts "Name: line" text into per-speaker audio. It largely reinforces rather than extends the already-detailed schema descriptions.

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?

Opens with a concrete verb+resource ("Generate a voiceover") and immediately scopes it (single voice vs. multi-speaker dialogue). It is clearly distinguishable from siblings like generate_music, generate_sfx, and transcribe_asset.

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

Usage Guidelines5/5

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

Explicitly states prerequisites ("Load builtin-voiceover first"), the async workflow ("start every generation you need... then call wait_for_jobs with all their Job IDs"), the downstream step ("patch_film"), and an alternative (transcribe_asset for word times). It also gives an anti-pattern: put one voiceover or whole dialogue in one call rather than splitting.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Vidova toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources