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Get several transcripts

get_transcripts
Read-onlyIdempotent

Fetch transcripts for up to 20 YouTube videos in one call, with missing captions reported instead of failing the batch. Use it to compare openings or build a corpus for analysis.

Instructions

Fetch transcripts for up to 20 videos in one call. A video with no captions is reported in place rather than failing the batch. Use it to compare how a set of videos open, or to build a corpus before analyzing it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videosYesUp to 20 video ids or URLs.
languageNo
max_chars_eachNoTruncate each transcript to this many characters. Useful when you only need the openings.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and open-world, so safety is covered. The description adds genuinely useful batch semantics — a captionless video is reported in place instead of failing the whole call — which is exactly the partial-failure behavior an agent needs before batching. It omits any language-fallback behavior, 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with the capacity and scope, then the failure semantics, then the use cases. No sentence is redundant with the others.

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?

For a read-only batch fetch with no output schema, the description covers capacity, batch-failure behavior, and intended uses. The remaining gap is the language parameter — no default, no fallback when a requested language is unavailable — which an agent would need to call correctly.

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 67%, which already documents the videos item format and the maxItems cap plus the truncation intent for max_chars_each. The description only restates the 20-video limit and the 'openings' use case; the language parameter is undocumented in both schema and description, leaving real ambiguity.

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 (fetch), resource (transcripts), and batch scope (up to 20 videos in one call). The batch framing and the 20-item cap clearly separate it from the singular get_transcript sibling without needing to name it.

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 two concrete use cases: comparing video openings and building a corpus before analysis. However, it never states when not to use it (e.g., a single video should go to get_transcript) and names no alternative sibling, so the routing guidance is directional but not exhaustive.

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