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google_trends_search: GET /

hasdata_google_trends_search_getTrendsData

Retrieve Google Trends data for search queries with geo targeting, date range, and category filters. Get interest over time, geo maps, and related topics or queries to inform keyword strategy, demand forecasting, and content planning.

Instructions

Get Google Trends Data

Pulls Google Trends data for one or more queries with geo targeting, region granularity (country/subregion/metro/city), date range, category, time zone, Google property (web, images, news, shopping, YouTube), and dataType (timeseries, geoMap, relatedTopics, relatedQueries). Returns interest-over-time series, geo-level breakdowns, and rising/top related topics/queries with relative scores. Use for keyword/content strategy, demand forecasting, seasonality analysis, topic discovery, campaign timing, and adding live-trend signals to marketing or research agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSpecify the search term for which you want to retrieve trends data.
geoNoSpecifies the location for the search. Defaults to Worldwide if not set or empty.
regionNoUsed to get more specific results when using "Interest by region" data type. Other data types do not accept this parameter. The default value depends on the geo location that is set. Available options: - `country`: Country - `region`: Subregion - `dma`: Metro - `city`: City Note: Not all region options will return results for every geo location.
dataTypeNoDefines the type of search to perform. Available options: - `timeseries`: Interest over time (default). Accepts both single and multiple queries per search. - `geoMap`: Interest by region. Accepts both single and multiple queries per search. - `relatedTopics`: Related topics. Accepts only single query per search. - `relatedQueries`: Related queries. Accepts only single query per search.
tzNoDefines a time zone offset in minutes. The default value is 420 (Pacific Daylight Time (PDT): UTC-7). The valid range for this parameter is from -1439 to 1439. To calculate the `tz` value for a specific time zone, you can use the time difference between UTC +0 and the desired time zone. Examples: - `420`: Pacific Daylight Time (PDT) - `60`: Central European Time (CET) - `-540`: Japan Standard Time
catNoCategory of the search term. The default value is 0 ("All categories").
gpropNoSorts results by a specific property. The default property is Web Search (applied when the gprop parameter is not set or empty). Available options: - `images`: Image Search - `news`: News Search - `froogle`: Google Shopping - `youtube`: YouTube Search
dateNoDefines a date range for the search. Available options: - `now 1-H`: Past hour - `now 4-H`: Past 4 hours - `now 1-d`: Past day - `now 7-d`: Past 7 days - `today 1-m`: Past 30 days - `today 3-m`: Past 90 days - `today 12-m`: Past 12 months - `today 5-y`: Past 5 years - `all`: 2004 - present You can also specify a custom date range using one of the following formats: - `yyyy-mm-dd yyyy-mm-dd` - (e.g. 2021-10-15 2022-05-25) for dates from 2004 to present. - `yyyy-mm-ddThh yyyy-mm-ddThh` - (e.g. 2022-05-19T10 2022-05-24T22) for dates with hours within a week range. The hours will be calculated based on the tz (time zone) parameter.
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 important constraints like 'relatedTopics and relatedQueries accept only single query' and region granularity limitations. However, it lacks information on rate limits, authentication, data freshness, or other operational traits.

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 well-structured, starting with a succinct title, then a dense paragraph covering functionality and return types, followed by use cases. Every sentence adds value without redundancy.

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 tool's complexity (8 parameters, no output schema), the description explains the main data types, return values, and provides usage contexts. It lacks details on output structure and error handling but covers enough for an agent to invoke correctly.

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 baseline is 3. The description's main paragraph summarizes parameters (geo, region, date, etc.) but does not add significant new meaning beyond the schema descriptions. The schema already contains detailed parameter explanations.

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 'Get Google Trends Data' and details the specific data types and return values (interest-over-time, geo breakdowns, related topics/queries). It is distinct from sibling tools which focus on other data sources like Amazon, Google Maps, etc.

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 lists explicit use cases such as 'keyword/content strategy, demand forecasting, seasonality analysis', providing clear context for when to use this tool. It does not explicitly mention when not to use it or compare with alternatives, but the sibling tools are sufficiently different.

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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