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TikTok Comment Replies

tiktok_comment_replies
Read-only

Get the replies to one TikTok comment (its thread) by video URL or numeric video ID (provide exactly one) plus the comment ID from tiktok_video_comments. Returns each reply's text, author profile, like count and date, about 50 per page. Billed $0.006 per page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoTikTok video URL, e.g. https://www.tiktok.com/@tiktok/video/7516594811734854943 (max 500 characters). Short links are accepted. Provide exactly one of url or video_id.
pagesNoNumber of pages to fetch, 1-1000 (default: 1). Each page returns up to 50 replies.
video_idNoNumeric TikTok video ID: the number at the end of a video URL. Provide exactly one of url or video_id.
comment_idYesNumeric ID of the comment, as returned by Video Comments
get_sentimentNoAdd AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small surcharge per page, or per request on single-request endpoints.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, openWorld, non-destructive), so the bar is lower. The description adds genuinely useful behavior: ~50 replies per page, $0.006 per page billing, and that get_sentiment adds a surcharge. It does not mention auth requirements or error behavior, but the cost/pagination disclosure is real added value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Two dense sentences that are front-loaded with the purpose and end with the return contents and cost; every clause carries information (input constraint, source of comment_id, per-page size, billed rate). Slightly packed but no filler.

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 carries the burden of describing returns and does so (reply text, author profile, like count, date), plus pagination and pricing. It omits any mention of the get_sentiment parameter's effect on output, which is a minor gap for a 5-parameter tool.

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 description coverage is 100%, so the schema already documents url, video_id, pages, comment_id, and get_sentiment in detail. The description only restates the url/video_id exclusivity and the ~50-per-page size, adding little beyond the structured fields.

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 ('Get the replies to one TikTok comment (its thread)') and scopes it to a single comment thread, which cleanly distinguishes it from the sibling tiktok_video_comments that produces the comment_id.

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?

It establishes the workflow context by requiring the comment ID 'from tiktok_video_comments' and states the exactly-one-of url/video_id constraint, so the agent knows the prerequisite and the input contract. It stops short of naming explicit when-not-to-use conditions or other alternatives.

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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