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Align Words

align_words

Get the word times for a take you already have, so captions and cuts land on the word. Give it the recording (an asset id from your library, or an https link) and the script that is spoken in it, and every word comes back with the second it starts and the second it ends. Use this when you already know the words; use transcribe_audio when you do not. Send the script as plain spoken words with no speaker names in front of the lines, or the times come back wrong. Billed per minute of the recording (a charge applies). Nothing new is saved to your library.

Input Schema

NameRequiredDescriptionDefault
textYesThe words spoken in the take, exactly as they are said. No "NAME:" labels in front of lines.
recordingYesThe take the words belong to: an asset id from your library, the generation_id any Switch audio tool gave back (list_my_audio shows them), or an https link.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / recording / description
      Previous value: -"The take the words belong to: an asset id from your library, or an https link."New value: +"The take the words belong to: an asset id from your library, the generation_id any Switch audio tool gave back (list_my_audio shows them), or an https link."
  2. Added

TDQS

A4.6/5.0
Behavior4/5

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

Adds real context beyond the annotations: billing is per minute of recording with a charge applied, and nothing is persisted to the library. That covers cost and side effects the readOnlyHint/openWorldHint flags do not convey. Missing is any sense of how long alignment takes or how failures surface.

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-loaded with the outcome and the inputs, then routing, then constraints, then billing. Sentences earn their place, though the 'captions and cuts land on the word' framing and the explicit return-value sentence overlap slightly.

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?

With no output schema, the description supplies the return shape (each word with start and end seconds), the cost model, the persistence behavior, and the main input pitfall. That is everything an agent needs to call this correctly.

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 100%, so the baseline is 3, but the description adds meaning the schema lacks: the script format warning is tied to an observable failure mode ('times come back wrong'), and it enumerates acceptable recording sources in prose. It does not add format syntax beyond what the properties already say.

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: get word-level start/end times for a given recording plus its script. It also explicitly contrasts with transcribe_audio, so an agent can separate the two without opening 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 Guidelines5/5

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

Gives an explicit decision rule: use this when you already know the words, use transcribe_audio when you do not. It adds a second usage constraint (script must be plain spoken words with no speaker labels) and names the consequence of violating it.

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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