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tiktok_mobile_comments

Retrieve top-level TikTok video comments with pagination, user details, text, like counts, and reply totals so you can analyze audience engagement or moderate discussions.

Instructions

Retrieve comments for a TikTok video. Returns the top-level comments of one video as the mobile app returns them: each comment has its full user projection, text, digg_count and reply_comment_total, plus cursor, has_more and total. This route needs a signed-in app session and throttles independently of the replies route. A page with no comments (including an empty body) is returned as an empty result, because that is the documented degraded shape in a throttle window.

Input Schema

NameRequiredDescriptionDefault
countNoComments per page (max 50)
cursorNoPagination cursor
aweme_idYesVideo id to list comments for

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.16

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and delivers real behavioral facts: it requires a signed-in app session, throttles independently of the replies route, and returns an empty result for an empty body because that is the documented degraded shape in a throttle window. Missing only ordering guarantees (e.g. top vs newest) and how cursor exhaustion should be handled.

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?

Front-loaded with purpose and scope, then response shape, then behavioral caveats. The final sentence about the empty-body failure mode is dense but earns its place; overall slightly long but no filler sentences.

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?

There is no output schema, and the description compensates by enumerating the returned fields (user, text, digg_count, reply_comment_total, cursor, has_more, total) plus the degraded empty-result case. Only remaining gap is pagination/ordering semantics.

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 aweme_id, cursor and count are already documented, including the max-50 cap. The description adds no syntax, format or default guidance for these parameters, so baseline 3 applies.

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 ('Retrieve comments for a TikTok video') and immediately scopes it ('top-level comments of one video'), which separates it from the replies sibling. An agent can pick this tool over tiktok_mobile_comment_replies without reading either schema.

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?

Clarifies the scope is top-level comments on a single video and notes the route 'throttles independently of the replies route', which hints at the alternative without an explicit when-to-use rule. Context is clear but no explicit exclusion such as 'do not use for reply threads'.

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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