Google Trends MCP Server

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find_topic

Read-onlyIdempotent

Resolve any text into matching Google Trends topic IDs, so you can query by concept instead of exact keyword and capture all spellings, languages, and meanings.

Instructions

Find the Google Trends topics for a piece of text and the ids that select them.

A search term ("tesla") counts only searches containing that exact wording, in one language. A topic ("Tesla — Automotive company", id /m/0dr90d) counts every search about the thing, in any language and spelling, and leaves out other meanings (the band, the inventor). Pass a topic id wherever a keyword is expected.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "find_topic"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds semantic transparency by explaining how topics count searches versus exact terms, which helps an agent understand the tool's results, though it omits operational details like rate limits.

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?

Four sentences, front-loaded with purpose, then a concise explanation, then a usage directive. No redundant or filler content; every sentence serves the explanation.

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?

Given an existing output schema and comprehensive safety annotations, the description provides sufficient context: what the tool does, the distinction from keyword search, and how to use the returned ids. No critical gaps for an agent to invoke it 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 coverage is 0% for the single 'text' parameter. The description implies the parameter is a search term/keyword and gives an example ('tesla'), but does not specify format, whether phrases are allowed, or how the text maps to returned topics, so it only partially compensates.

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?

States a specific verb (Find) and resource (Google Trends topics), and clarifies output (ids that select them). It distinguishes from keyword search by contrasting search terms and topics, making the tool's scope unmistakable.

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?

Explains the conceptual difference between search terms and topics and instructs to pass a topic id wherever a keyword is expected. This provides clear context for when to use the output, but does not name sibling tools or explicit exclusions.

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