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HasData

HasData MCP Server

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tiktok_comments: GET /

hasdata_tiktok_comments_getTikTokComments

Fetch TikTok video comments or comment replies by videoId or commentId to collect text, likes, timestamps, reply counts, and author data for sentiment analysis and engagement research.

Instructions

Get TikTok Comments

Fetches the comments on a public TikTok video by its numeric video id, or the replies to a specific comment when commentId is given. Each comment returns text, like count, timestamp, reply count, and author (username, nickname, avatar, plus hasdataLink to the profile endpoint and hasdataPostsLink to the posts endpoint). Supports token-based pagination via nextPageToken. Use for sentiment analysis, engagement research, or building comment datasets from a video discovered via the posts or search APIs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoIdYesThe numeric id of the video (the number after `/video/` in a TikTok URL).
commentIdNoWhen provided, returns the replies to this comment instead of the video's top-level comments.
nextPageTokenNoDefines the next page token. Use the `nextPageToken` value returned by the previous response. Omit it to fetch the first page.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.2

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden and explains read-only fetching of public comments, pagination via nextPageToken, and the returned comment fields. It does not mention rate limits or error behavior, but the disclosed behavior is clear.

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 compact, well-organized, and free of filler. Key information about input, output fields, pagination, and use cases is front-loaded.

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?

Although there is no output schema, the description enumerates returned comment fields and pagination behavior, which gives an agent enough context for typical use. It lacks response envelope or error details, but those are not essential for selection.

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

Parameters5/5

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

All parameters have descriptive schema entries, and the description adds practical context: videoId is the number after /video/ in a TikTok URL, commentId selects replies, and nextPageToken uses the previous response value with omission for the first page.

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?

Clearly states it fetches TikTok comments by video ID or replies by comment ID, distinguishing it from sibling TikTok profile, posts, and search tools. The verb 'Fetches' and the resource 'comments on a public TikTok video' make the purpose specific.

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?

Provides concrete use cases such as sentiment analysis, engagement research, and building comment datasets, and notes that videos may be discovered via posts or search APIs. It does not explicitly contrast with sibling tools, but the guidance is sufficient for typical selection.

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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