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List playlist videos

list_playlist_videos
Read-only

List the items in a playlist. Accepts a YouTube playlist ID or URL or a TikTok playlist URL (tiktok.com/@user/playlist/name-id). Every row's url can be passed straight to get_transcript.

Input Schema

NameRequiredDescriptionDefault
limitNoMax results to return (1–50). Defaults to 5.
playlistYesYouTube playlist ID or URL, or a TikTok playlist URL.

Output Schema

NameRequiredDescriptionDefault
countNoNumber of videos returned.
noticeNoWhy a list is empty or may be incomplete, when the source says: e.g. "This channel has no Live tab."
videosNoMatching videos with ids, titles, and URLs.
balanceNoRemaining credit balance after this call, when known.
creditsSpentNoCredits charged for this call (0 on failure or for admins).
creditsNoticeNoCredits words for the user, with the link that acts on them: why the call was refused for credits, or a note once the balance runs low. Pass it on to the user.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint, destructiveHint=false, openWorldHint) so the description needn't restate them. It adds real context beyond them: dual-platform coverage (YouTube and TikTok) and the shape of each row (a url), which drives the downstream workflow. Ordering, pagination depth, and behavior on private/deleted playlists remain undisclosed.

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 no filler. The core action is front-loaded, followed by accepted inputs and the downstream handoff — exactly the order an agent needs.

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 an output schema present, return values needn't be spelled out, and the description still surfaces the useful part of the return shape (row urls). For a 2-parameter read tool this is close to complete; only edge-case behavior (private playlists, item ordering) is unaddressed.

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 format detail the schema lacks — a concrete TikTok playlist URL pattern and the platform split for the `playlist` parameter. The `limit` parameter is left entirely to the schema, which is acceptable at full coverage.

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?

States a specific verb and resource ("List the items in a playlist") and names the two supported platforms, which distinguishes it from list_channel_playlists (returns playlists, not items). It stops short of explicitly naming siblings, so the differentiation is inferable rather than stated.

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 concrete accepted input forms (YouTube playlist ID or URL, TikTok playlist URL with a worked pattern) and an explicit follow-on step: "Every row's url can be passed straight to get_transcript." It doesn't state when not to use it or what to do for other platforms, but the usage context is clear.

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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