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jain-eshan

Google Trends MCP

by jain-eshan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

interest_by_region

Get relative search interest for a keyword broken down by state/region or country. Specify geo and timeframe to see which areas show highest interest, aiding market research.

Instructions

Search interest for a keyword broken down by state/region within a country, or by country when geo is "" (worldwide).

Args: keyword: a single search term. timeframe: pytrends timeframe string, e.g. "today 12-m". geo: ISO country code (e.g. "US", "IN"), or "" for worldwide (default). response_format: "concise" (default) returns only the top 10 regions by interest, rounded to whole numbers. "full" returns every region, unrounded.

Returns: A list of records, one per state/region within the specified geo, each containing: - "geoName": the state/region name (e.g. "California"), or the country name when worldwide - A column with the keyword name as the key: relative search interest (0-100 scale) for that region. Higher values indicate higher relative interest in that region compared to others in the same country. This is Google Trends' standard region-relative scale.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNo
keywordYes
timeframeNotoday 12-m
response_formatNoconcise

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • changedInput schema / properties / geo / default
      Previous value: -"IN"New value: +""
    • addedInput schema / properties / response_format
      Added value: +{
+  "default": "concise",
+  "title": "Response Format",
+  "type": "string"
+}
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the output format, the scale (0-100) and its meaning, and the difference between 'concise' and 'full' response formats. It also notes the relative nature of interest scores. It does not mention potential limitations (e.g., rate limits, data availability), but the core behavior is transparent.

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 well-organized with a clear opening purpose, followed by args and returns. It is detailed but not verbose; every sentence adds value. The structure is logical, though the args/returns section is somewhat lengthy, but that is justified given the need to explain the output format.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 4 parameters, no output schema, and no annotations. The description covers all parameters, explains the output structure precisely, and gives examples. It is complete enough for an agent to call correctly without further information. No critical information is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must explain all parameters. It does so thoroughly: keyword (single search term), timeframe (with example 'today 12-m'), geo (ISO codes, '' for worldwide), and response_format (with definitions of 'concise' and 'full'). It adds meaning well beyond the schema's bare types.

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 purpose: searching interest for a keyword broken down by state/region within a country, or by country when geo is empty. This is a specific verb+resource combination that distinguishes it from sibling tools like interest_over_time (time series) and related_topics (related queries).

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 clearly indicates when to use this tool: when you need regional breakdown of interest within a country, or by country worldwide. It does not explicitly state when not to use it, but the context is clear and the parameter descriptions (geo, timeframe) provide implicit guidance. The return format explanation further clarifies usage.

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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