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Google Trends MCP Server

google_trends_search: GET /

hasdata_google_trends_search_getTrendsData

Fetch Google Trends data for queries: interest over time, regional interest, related topics and queries. Supports keyword research, seasonality analysis, demand forecasting, and content strategy.

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.
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"). Provide one exact documented value (1133 allowed), e.g. `0`, `3`.
geoNoSpecifies the location for the search. Defaults to Worldwide if not set or empty. Provide one exact documented value (3517 allowed), e.g. `AF`, `AF-BDS`.
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.
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
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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of explaining behavior. It implies a read-only data retrieval operation through words like 'Pulls' and 'Returns', and mentions no destructive side effects. However, it does not explicitly state that the operation is read-only, nor does it mention authentication, rate limits, or potential errors, leaving some behavioral aspects implicit.

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?

The description is concise and front-loaded with the core purpose, followed by a compact summary of parameters and use cases. It avoids redundancy and stays focused, though the final list of use cases adds length without critical operational detail. Overall it is well-structured and not overly verbose.

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?

Without an output schema, the description gives a reasonable overview of expected outputs (interest-over-time series, geo-level breakdowns, related topics/queries with relative scores). It also covers parameter capabilities and domain use cases. It does not describe response formats, pagination, or error conditions, but given the tool's complexity and lack of output schema, the description is fairly complete.

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?

All eight parameters have detailed descriptions, covering defaults, allowed values, and format examples. The q parameter is described as a singular 'search term' while the main description says 'one or more queries', leaving ambiguity about how to pass multiple terms. Most other parameters are very well explained, but this minor gap prevents a perfect score.

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 retrieves Google Trends data, enumerates the key parameters (geo, region, date, category, time zone, property, dataType), and distinguishes it from sibling tools by focusing specifically on Trends. The verb 'Pulls' and the listed outputs make the tool's purpose unambiguous.

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 use cases such as keyword/content strategy, demand forecasting, seasonality analysis, and topic discovery, which strongly signal when an agent should select this tool. It does not explicitly mention when not to use it or point to alternatives, but given there is no other Google Trends sibling tool, the guidance 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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