Skip to main content
Glama
jain-eshan

Google Trends MCP

by jain-eshan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

related_topics

Get top and rising related topics for a keyword to uncover Google Trends topic clusters and breakout growth.

Instructions

Top and rising related topics (Google's topic clusters, not raw query strings) for a single keyword.

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 of each list, sorted by "value" descending, rounded to whole numbers. "full" returns every row, unrounded.

Returns: A dict with two keys, each containing a list of topic records: - "top": most-searched related topics. Each record has "topic_title", "topic_type", "value" (0-100 relative interest on Google Trends scale). - "rising": fastest-growing related topics. Each record has "topic_title", "topic_type", "value". IMPORTANT: a value of 5000% is Google's "Breakout" marker, indicating explosive new growth from near-zero baseline, NOT a literal 5000% increase. This is the same convention as "rising" queries.

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

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and excels: it defines the output dictionary structure, explains the 'concise' vs 'full' formatting behavior, and explicitly warns that a value of 5000% is Google's Breakout marker, not a literal percentage increase. This goes well beyond a bare statement of what the tool does.

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 with Args and Returns sections, front-loads the primary purpose, and every sentence adds meaningful detail. The Breakout warning is prominent but earned, not filler.

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?

There is no output schema, so the description must explain return values; it does so with the dict keys, record fields, value scale, and format differences. For a tool with four parameters and no annotations, this is a complete and self-sufficient definition.

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%, and the description compensates completely. Every parameter is documented: keyword is a single search term, timeframe gets a pytrends example, geo gets ISO country examples and 'worldwide' behavior, and response_format gets detailed formatting and sorting rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource: top and rising related topics for a single keyword, and adds the crucial disambiguation 'Google's topic clusters, not raw query strings' against the related_queries sibling. It lacks an explicit verb like 'returns' or 'retrieves', so it stops just short of a perfect purpose statement.

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?

It states clear context: use for a single keyword and for Google topic clusters rather than raw query strings. The exclusion of raw query strings is an explicit when-not signal, though it never names related_queries directly or gives a fuller when-to-use/alternative comparison.

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

Deploy Server

Other Tools