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TokConnect: TikTok Research

explore_categories

List the categories currently available in TikTok Explore. Use each category's numeric type with explore_videos. Returns native categoryList metadata; these are Explore categories, not Creator Search Insights categories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
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. 'List the categories currently available' implies a read-only operation and it discloses the return payload ('native categoryList metadata'), but it says nothing about auth requirements, rate limits, or whether the list is cached/stable. Adequate but not rich for a tool with zero structured behavioral hints.

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?

Three short sentences, front-loaded with the purpose, then the follow-up action, then the disambiguation. No filler; each sentence adds a distinct piece of information.

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 partially compensates by naming the return payload ('categoryList metadata') and the key field ('numeric type') an agent needs for the next step. Combined with the sibling disambiguation, an agent has enough to call and use it correctly, though field-level detail is thin.

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?

The tool takes no input parameters, so there is nothing for the description to clarify on the input side; baseline 4 applies. The mention of the 'numeric type' field refers to the returned categories and is handled under contextual completeness rather than input semantics.

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 (List) and resource (categories in TikTok Explore), and explicitly disambiguates from two things an agent might confuse it with: the sibling explore_videos (as a consumer of the output) and the unrelated Creator Search Insights categories.

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?

Gives clear downstream usage guidance ('Use each category's numeric type with explore_videos'), which tells the agent why and when to call this tool. It does not state any exclusions or prerequisites, but for a zero-parameter listing tool the context is sufficient.

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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