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YouTube Transcript AI

by ai.youtube-transcript
OverviewDiscussionsAdmin

find_in_transcript

Find where a video talks about something. Give one or more keywords and get back only the matching passages, each with a timestamp and a link that opens the video at that moment. Far cheaper than reading the whole transcript when you are looking for specific points. Matching is a case-insensitive text match, so try synonyms and spelling variants (auto-captions often mis-hear names).

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>",
      "keywords": "<keywords>"
    },
    "name": "find_in_transcript"
  }
}

Input Schema

NameRequiredDescriptionDefault
langNoOptional BCP-47 caption language code. Omit for the default track.
videoYesYouTube video URL or 11-character video ID
contextNoParagraphs (about 30 seconds each) to include before and after each match. Default 1.
keywordsYesWords or short phrases to look for (up to 8). A passage matches if it contains any of them.
max_hitsNoMaximum passages to return. Default 8.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it does disclose the return shape (matching passages with timestamp and deep link) and the matching semantics (case-insensitive, so synonyms and spelling variants may be needed). Auth and error behavior are not mentioned, but for a read-only lookup this is largely sufficient.

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?

Four short sentences, front-loaded with the action and the return value, then the cost rationale, then the keyword-matching caveat. Nothing is wasted and each sentence contributes new information.

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?

With no output schema, the description appropriately explains what comes back (passages, timestamps, deep links). Combined with a well-documented schema it covers what an agent needs to call correctly; only minor behavioral edges like result-limit behavior are left implicit.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning about the keywords parameter: matching is a case-insensitive text match and auto-captions may mis-hear names, which tells an agent how to formulate keywords beyond what the schema states. It does not touch context, lang, or max_hits, relying on the schema for those.

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 ('Find where a video talks about something') and immediately contrasts with reading a whole transcript, which distinguishes it from get_youtube_transcript and get_transcripts_batch. An agent can pick this tool without opening another schema.

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 when-to-use signal ('when you are looking for specific points') and a comparative advantage ('far cheaper than reading the whole transcript'), which implicitly routes bulk-transcript use to the sibling tools. It stops short of explicit exclusions or naming the alternative tools directly.

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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