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YouTube Transcript AI

by ai.youtube-transcript
OverviewDiscussionsAdmin

get_spoken_context

Read what was said around a moment in a video: the sentences before and after a timestamp. Use it after search_spoken_words to see what a matching sentence was part of, before you quote or rely on it. Works for videos in the library only.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "video": "<video>",
      "seconds": "<seconds>"
    },
    "name": "get_spoken_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
videoYesYouTube video ID or URL, as returned by search_spoken_words.
windowNoSeconds to include before and after. Default 20.
secondsYesThe moment to read around, in seconds from the start of the video.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description must carry behavioral load; it does disclose that the operation is a read and is limited to library videos, which is useful access-scope context. It does not say what happens when a video has no transcript, when the timestamp exceeds duration, or how the sentences are returned.

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?

Three short sentences with zero filler; the core action and output shape come first, the usage trigger second, and the scope caveat last. Every sentence conveys a distinct, load-bearing fact.

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?

There is no output schema, and the description compensates by describing the return content (sentences before and after the timestamp). For a small read-only tool this is nearly complete, though error and edge-case behavior remain unaddressed.

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 coverage is 100%, so all three parameters (video, seconds, window) are already documented in the schema, establishing a baseline of 3. The description adds only light meaning, clarifying that 'seconds' marks the anchor of the before/after window and that 'video' comes from library/search results.

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 ('Read what was said around a moment in a video: the sentences before and after a timestamp') and immediately scopes it ('videos in the library only'). It also explicitly differentiates from the sibling search_spoken_words by framing itself as the follow-up step to that tool.

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?

Gives a clear workflow trigger: 'Use it after search_spoken_words... before you quote or rely on it,' which tells the agent when in a sequence to call it. The library-only constraint also rules out external videos, though it never states when the full-transcript sibling (get_youtube_transcript) would be preferable instead.

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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