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Vee3

by io.github.Vee3io

tiktok.video_comments

List comments on a TikTok video. Provide either video_id or video_url, not both. Pass cursor from a previous response to fetch the next page.

Cost = 2 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of comments to return (max 50).
cursorNoPagination cursor from a previous response.
video_idNoTikTok video id.
video_urlNoTikTok video URL.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
msgNoUpstream status message.
codeNoUpstream status code (0 = success).
dataNoCapability-specific payload from the upstream provider.
processed_timeNoUpstream processing time in seconds.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / properties / processed_time / anyOf
      Previous value: -[
-  {},
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "number"
+  },
+  {
+    "type": "null"
+  }
+]
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses cost (2 tokens) and pagination behavior, but does not mention potential errors, default behavior when no identifier is given, or auth requirements. Missing edge-case context makes it not fully transparent.

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 concise and well-structured: two sentences plus cost, all front-loaded. Every sentence adds value—purpose, critical parameter constraint, and pagination note—with no fluff.

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?

The output schema covers return format, and the description provides essential usage details (one-of constraint, pagination, cost). It doesn't explain edge cases like what happens if both or neither identifiers are provided, but overall it is sufficient for an agent to invoke the tool correctly.

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%, and the description adds important semantics beyond it: mutual exclusivity of video_id and video_url, and the meaning of cursor from a previous response. This compensates for the schema's minimal per-parameter descriptions.

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?

The description clearly states the tool lists comments on a TikTok video, using specific verbs and resource. It does not explicitly differentiate from sibling tools like tiktok.comment_replies, but the name and description make the distinction apparent.

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 provides explicit guidance on parameter usage: 'Provide either video_id or video_url, not both' and pagination via cursor. It does not mention alternatives or exclusions relative to other TikTok tools, but gives clear operational context.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a distinct purpose, further clarified by group prefixes and clear descriptions. Within each group, tools perform different operations (e.g., domains.lookup vs. domains.whois vs. domains.rdap) with no ambiguity.

Naming Consistency5/5

All tools follow a consistent group.tool_name pattern using snake_case. The naming is predictable and uniformly applied across all groups.

Tool Count4/5

78 tools is high, but the server aggregates multiple distinct API domains (11 groups). Each group has a reasonable number of tools, typically under 10, with TikTok having 17. The count reflects breadth, not bloat.

Completeness5/5

Each domain's tool set covers the primary expected operations (e.g., search, details, reviews, metrics, user info). There are no obvious gaps for read-only analytical use; features like posting are likely out of scope.

Resources