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Read captions on a clip

read_captions
Read-onlyIdempotent

Use this when the user asks what a clip says, what is on screen at a particular moment, which caption contains a word or a typo, or who said something. It is also the only way to get a caption's id, so read it first whenever a later step needs to name one caption. Narrow with at_seconds, a from/to range, or contains. Do not use for a transcript made with transcribe_audio, or to find clips (use get_episode).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoUp to 200. Default 40.
clip_idYesA made clip's clip_id, from get_episode or get_job.
containsNoOnly captions containing this text. Case-insensitive.
at_secondsNoOnly the caption(s) on screen at this moment, in seconds.
to_secondsNoEnd of a time range, in seconds.
from_secondsNoStart of a time range, in seconds.
include_removedNoInclude captions the editor has removed. They are not in the video. Default false.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
totalNoCaptions matching, before the limit.
clip_idYes
messageYes
captionsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful non-schema context: it is the only route to a caption id and should be called before any dependent step. It does not discuss result volume or pagination behavior beyond the schema's limit, hence not a 5.

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 primary use case and efficiently packed; the sequencing rule and the exclusion clause each earn their place. It reads slightly list-like with multiple parallel question framings, but there is no filler or redundancy.

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 full schema coverage, a rich annotation set, and an output schema, the description only needs to cover selection and sequencing, which it does thoroughly. Nothing an agent needs to invoke this correctly is missing.

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 every parameter is already documented with type, default, and meaning. The description restates the three narrowing parameters (at_seconds, from/to range, contains) without adding semantics beyond the schema, which matches the baseline 3 when the schema does the heavy lifting.

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?

The description states a specific verb (read) and resource (captions on a clip) and enumerates the question types it answers, making its scope concrete. It explicitly distinguishes itself from transcribe_audio output and from get_episode, so an agent can separate it from siblings without opening schemas.

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?

It gives explicit when-to-use cases (what a clip says, on-screen moment, word/typo lookup, speaker attribution), an ordering rule (read first to obtain caption ids), narrowing strategies, and explicit when-not-to-use guidance naming the correct alternatives (transcribe_audio, get_episode).

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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