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transcribe

Generate word-level timestamps for a project asset and cache the transcript for retrieval with get_transcript, enabling captions and text-based editing.

Instructions

Transcribe an asset with word timestamps (needs pip install reelwright[transcribe]). Results are cached; read them with get_transcript.

Input Schema

NameRequiredDescriptionDefault
modelNobase
projectYes
asset_idYes
languageNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose two real behaviors: an optional dependency install is required, and results are cached rather than returned for direct consumption. It omits whether this is long-running or expensive, whether it mutates project state, and how the model choice affects speed/accuracy.

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 action and its output, then the prerequisite and the read-back path. Nothing is wasted or buried.

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?

For a mutation-style tool with no output schema and zero-documented parameters, the definition covers the call/read workflow and the install requirement, which is the most important framing. It still leaves open the parameter tradeoffs (model, language) and whether the call blocks or returns a status, so an agent can call it but not call it well.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate and largely does not. It implies asset_id via 'an asset' but says nothing about the model (default 'base'), language (nullable, presumably auto-detect), or project parameter, leaving three of four parameters without meaning beyond their names.

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 gives a specific verb and resource ('Transcribe an asset') and adds the distinguishing output trait 'word timestamps', which separates it from get_transcript (reading) and import_transcript (bringing in existing transcripts). It stops short of explicitly naming those siblings as alternatives, but the object is unambiguous.

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?

It states the prerequisite ('needs pip install reelwright[transcribe]') and the follow-up pattern ('Results are cached; read them with get_transcript'), so an agent knows both what to install and what to call next. No explicit when-not guidance or cost/runtime expectation is given.

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