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TrendFlow

by dev.mory

Related queries

get_related_queries
Read-only

Find the top and rising searches related to a term. Use this for keyword discovery, SEO and content research, and spotting breakout queries. top is ranked by volume; rising is ranked by growth, where "Breakout" means growth too large to measure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNoCountry code such as "US", "GB", "TH"; a sub-region such as "US-CA"; or a US metro code such as "807". Empty string means worldwide.
keywordYesA search term, or a topic id from search_topics (e.g. "/m/0mkz").
categoryNoGoogle Trends category id to restrict the query to; 0, the default, is every category. Narrowing disambiguates a word with several meanings without needing a topic id.
timeframeNoTime range. A preset, or a custom range as two ISO dates: "2023-01-01 2023-06-30". The range also sets granularity — hourly ranges return minute-level points and "all" returns monthly ones, so a long range cannot show a short spike.
searchPropertyNoWhich Google surface to measure: empty for web search, or images, news, youtube, or froogle (Shopping). These are separate indexes, so values from different properties are not comparable to each other.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is known. The description adds valuable behavioral context by explaining the ranking semantics: 'top is ranked by volume; rising is ranked by growth, where "Breakout" means growth too large to measure.' This goes beyond the annotations.

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 that front-load the purpose, specify use cases, and define key terms. No wasted words.

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?

Given the rich schema and annotations, the description provides sufficient context for a read-only research tool. It covers the tool's output semantics (top/rising) and use cases. However, it does not describe the return format or any limitations, which would be helpful since there is no output schema.

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?

The input schema has 100% parameter descriptions, so the schema already explains all parameters. The description does not add parameter-specific semantics beyond what the schema provides, so it meets the baseline for high schema coverage.

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?

The description clearly states the tool's function: 'Find the top and rising searches related to a term.' It specifies the resource (related searches) and the action (find), and distinguishes from siblings by mentioning keyword discovery, SEO, and breakout queries, which hints at its unique role among related tools.

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 gives explicit use cases: 'Use this for keyword discovery, SEO and content research, and spotting breakout queries.' This provides clear context for when to use the tool, though it does not name alternative tools or explicitly state when not to use it.

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

A4.3/5.0
Disambiguation4/5

Each tool targets a distinct Google Trends data type—regional, temporal, related queries, trending now, topic resolution—so agents can usually tell them apart. However, research_trend overlaps with three of the get_* tools by combining their outputs, so it could be selected instead of a specific tool if the agent wants just one slice. Search topics is clearly separate.

Naming Consistency4/5

Four tools consistently use the get_verb_noun pattern (get_interest_over_time, etc.), but research_trend and search_topics deviate with different verbs while still keeping snake_case verb_noun. The pattern is readable but not perfectly uniform.

Tool Count5/5

Six tools is an appropriate number for a Google Trends server, covering the main interest endpoints plus a convenience aggregator and a topic resolver. Not too many, not too few.

Completeness4/5

The server covers the core Google Trends features: time series, regional breakdown, related queries, trending now, and topic resolution. A notable gap is the absence of a related_topics endpoint (topics related to a keyword), which complements related queries. Overall, the surface is fairly complete for typical trend research tasks.

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