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Glama

captions

Transcribe narration audio into word-timed subtitle tracks for demo videos. Returns a job ID immediately; poll job status to retrieve the result.

Instructions

Transcribe narration.mp3 to captions with word timing. Returns a jobId immediately — poll job_status. Rejects if another job is running.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dirYesdemo project directory — pass an absolute path
langNorender a language variant from scene.narrations[<code>] over the shared take (artifacts namespaced: audio/<code>/, captions.<code>.*, output/final-demo.<code>.mp4). Omit for the default single-language render.
paramsNostoryboard template params (name → value); each must be declared in the storyboard's params block. Substitutes {{name}} across all stages.
offlineNoapproximate captions from the script — no network/STT

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYes
jobIdYes
statusYes
demoDirYesresolved absolute demo directory
logFileYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses asynchronous behavior (immediate jobId), the polling endpoint, and a concurrency restriction. It does not mention side effects like artifact creation, but the output schema partially covers expected results.

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 short sentences, front-loaded with the core action and result, followed by the most important behavioral caveats. Every word earns its place.

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?

Covers what the tool does, the asynchronous lifecycle, and a critical failure condition. Parameter docs, output schema, and sibling job_status fill the remaining gaps. It could be more explicit about what 'captions' output looks like or what 'another job' refers to, but it is not incomplete enough to confuse an agent.

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 schema already documents all four parameters. The description adds minimal parameter-level meaning beyond noting the fixed input file name 'narration.mp3', which is a small contextual bonus.

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 ('Transcribe'), a specific source resource ('narration.mp3'), and the produced result ('captions with word timing'). This clearly distinguishes it from sibling tools like render, voice, or compose.

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?

Tells the agent the expected follow-up action ('poll job_status') and a key constraint ('Rejects if another job is running'). It does not explicitly contrast with alternatives, but the sibling set is broad and the core workflow is clear enough.

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