Scrapingdog MCP Server

Google Trends API

google_trends

Track keyword search interest over time and by region to uncover search volume patterns. Specify custom queries, time ranges, and locations.

Instructions

Track keyword search interest over time and by region via Google Trends.

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": {
      "query": "<query>"
    },
    "name": "google_trends"
  }
}

Input Schema

Name	Required	Description
`tz`	No	Timezone offset in minutes (-1439..1439). (API default: 420)
`cat`	No	Category id. (API default: 0)
`geo`	No	Location origin (e.g. US, GB). Defaults to worldwide.
`date`	No	Time range: e.g. 'now 1-H', 'now 7-d', 'today 12-m', 'all', or 'yyyy-mm-dd yyyy-mm-dd'.
`gprop`	No	Property filter.
`query`	Yes	Up to 5 comma-separated terms (1 for map-only types); max 100 chars each.
`region`	No	Map granularity.
`language`	No	Result language code (e.g. en, es, fr, de). (API default: en)
`data_type`	No	Type of Trends data. (API default: TIMESERIES)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Aug 1, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries full responsibility for behavioral transparency. It only states the basic function and provides no details about output format, data limitations, rate limits, or how the data is normalized. The lack of an output schema makes this gap more significant, as the agent has no insight into what the tool returns.

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 a single, clearly front-loaded sentence that conveys the core purpose in under 20 words. Every word is essential, with no fluff or redundancy. It is a model of conciseness.

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

Completeness2/5

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

Despite high schema coverage, the tool has 9 parameters, no output schema, and no annotations. The description provides only a high-level purpose and fails to explain the nature of the response (e.g., timeseries or map data, value ranges, normalization). Given the tool's complexity, the description is too minimal to fully inform an agent about expected behavior and return value.

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 the baseline is 3 even though the description itself does not elaborate on parameters. The tool description adds no extra meaning beyond the schema, but the schema already documents each parameter (e.g., query constraints, language, data_type enums). The description does not need to compensate for missing parameter information.

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 function: 'Track keyword search interest over time and by region via Google Trends.' It uses a specific verb ('track') and resource ('keyword search interest'), and the phrase 'over time and by region' distinguishes it from sibling search tools by focusing on trend analysis rather than general search results.

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 implies the tool is for trend analysis, not general search, but does not explicitly mention when to use it over alternatives or provide exclusions. The context is clear enough given the sibling tools (e.g., google_search), but it lacks explicit guidance on use cases or boundary conditions.

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