Skip to main content
Glama

TokConnect: TikTok Research

related_queries

CSI related-query deep dive — retrieve TikTok's related_deep_dive response for one CSI queryId. Use for adjacent search phrases and query exploration. Unlike related_topics, this returns the endpoint's native payload rather than a normalized, paginated topic list. Inspect returned fields; do not assume every phrase has a queryId or search-volume metric. No pagination parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_idYesCSI topic queryId returned by search_topics, browse_topics, trending_topics or related_topics. Not a video ID, username or keyword.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
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 does well: it discloses that the return is the endpoint's raw native payload, that there is no pagination parameter, and warns that not every phrase carries a queryId or search-volume metric. It stops short of describing the actual payload shape, but the caveats are genuinely useful.

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?

Four tight sentences, front-loaded with the core action and the sibling contrast. Each sentence contributes a distinct fact (contrast, payload nature, field caveat, pagination absence) with no padding.

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?

For a single-param tool with no annotations and no output schema, the description supplies the routing, the payload nature, and the expectation-setting caveats an agent needs. Only the concrete return field structure is left unspecified, which the description explicitly tells the agent to inspect at runtime.

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% so the queryId source is already documented, but the description adds non-obvious information: that there is no pagination parameter (preventing a failed call attempt) and that results should not be assumed to have a queryId or volume metric.

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 TikTok's related_deep_dive response for one CSI queryId') and explicitly distinguishes itself from the sibling related_topics. An agent can select it without opening the 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?

Gives a clear use case ('adjacent search phrases and query exploration') and names the alternative tool (related_topics) with the condition that differentiates them (native payload vs normalized paginated list). No explicit when-not-to-use case beyond that contrast, but the routing guidance is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources