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aaronckj
by aaronckj

scan_comments

Scan YouTube comments for banned words and automatically apply moderation actions such as hold for review or reject. Optionally limit to new comments with incremental mode.

Instructions

Sweep comments (one video, or channel-wide when video_id omitted) for banned words. auto_action: none | heldForReview | rejected — matches get that moderation status applied (dry_run previews instead). incremental=True only examines comments newer than the previous incremental scan (watermark stored locally) — cheap to run daily.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
dry_runNo
video_idNo
auto_actionNonone
extra_wordsNo
incrementalNo
Behavior4/5

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

No annotations provided, so the description carries full burden. It discloses moderation application, dry_run preview, incremental scanning with local watermark, and cost implications ('cheap to run daily'). Missing details on permissions or idempotency, but overall informative.

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?

The description is two concise sentences that front-load the core purpose. Every part adds value, with no superfluous words.

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?

Given 6 parameters, no output schema, and no annotations, the description provides a solid understanding of behavior and key parameters. It lacks explanation of the return value and some minor parameters, but is sufficient for a scanning tool.

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 description coverage is 0%, but the description adds meaning for 4 of 6 parameters: video_id (scope), auto_action (values), dry_run (preview), incremental (behavior). It does not explain limit or extra_words, but coverage is good for non-obvious parameters.

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 clearly states the tool scans comments for banned words, with scope (one video or channel-wide) and key parameter effects. It effectively distinguishes from sibling tools like moderate_comment or list_comments by focusing on automated scanning and moderation.

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?

The description explains when to use the tool (scanning for banned words) and provides guidance on auto_action and incremental modes. It does not explicitly list when not to use it, but the context from sibling tools makes differentiation 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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