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captions_transcribe

Transcribe audio or video into captions locally with Whisper CLI. No API key required; models download on first use.

Instructions

Transcribe locally with the Whisper CLI, if installed. No key; the model is downloaded on first use. Not in the base image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
modelNobase
languageNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses local execution, no API key needed, model download on first use, and the installation caveat. This is substantive behavioral context, though it does not describe the output format or error behavior.

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?

The description is succinct, with three sentences that front-load the core action and then add critical constraints. Every sentence carries meaningful information without fluff.

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

Completeness2/5

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

Given no annotations, no output schema, and no parameter descriptions in the schema, the description is incomplete. It explains installation and key requirements but fails to clarify what the tool expects (e.g., audio file path) or returns (e.g., transcript text), which an agent needs to call it correctly.

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

Parameters1/5

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

Schema description coverage is 0%, so the description should compensate by explaining parameters. It does not mention input, model, or language at all, leaving all three parameters undocumented beyond their bare names and types. This is a critical gap.

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 states a clear verb ('Transcribe') and resource ('Whisper CLI'), indicating the tool performs transcription. It doesn't explicitly distinguish it from sibling tools like captions_write_srt, but the action is unambiguous for a transcription task.

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

Usage Guidelines3/5

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

It gives a condition for use ('if installed') and notes the tool is not in the base image, which hints at an installation prerequisite. However, it does not mention alternatives or scenarios where another tool should be used, leaving usage context partial.

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