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TrendPulse - Google News & Trends

by AKzar1el
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_trends

Read-only

Analyze historical Google Trends interest-over-time for known keywords, comparing normalized 0-100 scores across timeframes to track trajectory.

Instructions

Return historical Google Trends interest-over-time points for one or more known keywords. Use this for trajectory and comparisons across a timeframe; values are normalized 0-100 interest scores, not absolute search volume. Use get_trending_terms for what is trending now.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
catNoGoogle Trends category ID; use 0 for all categories or a value from get_categories.
geoNoGeographic region code (e.g. 'US').US
sourceNoSearch source.google search
keywordYesSearch keyword(s) to analyze; comparison lists support 1-5 keywords.
data_modeNoLegacy resolution hint used only when timeframe is omitted.weekly
timeframeNoExplicit TrendsPy range, for example 'today 12-m', 'today 90-d', 'all', or 'YYYY-MM-DD YYYY-MM-DD'. Overrides data_mode when supplied.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv0.2.22
    • changedInput schema / properties / data_mode / description
      Previous value: -"Legacy resolution hint used only when timeframe is omitted: 'weekly', 'daily', 'monthly'."New value: +"Legacy resolution hint used only when timeframe is omitted."
    • addedInput schema / properties / data_mode / enum
      Added value: +[
+  "weekly",
+  "daily",
+  "monthly"
+]
    • changedInput schema / properties / keyword / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  }
-]New value: +[
+  {
+    "minLength": 1,
+    "type": "string"
+  },
+  {
+    "items": {
+      "minLength": 1,
+      "type": "string"
+    },
+    "maxItems": 5,
+    "minItems": 1,
+    "type": "array"
+  }
+]
    • changedInput schema / properties / keyword / description
      Previous value: -"Search keyword(s) to analyze."New value: +"Search keyword(s) to analyze; comparison lists support 1-5 keywords."
    • changedInput schema / properties / source / description
      Previous value: -"Search source: 'google search', 'youtube search', 'news search', 'image search', 'google shopping'."New value: +"Search source."
    • addedInput schema / properties / source / enum
      Added value: +[
+  "google search",
+  "youtube search",
+  "news search",
+  "image search",
+  "google shopping"
+]
  2. Changed1 schema field changedv0.2.13
    • removedOutput schema / description
      Removed value: -"Generic wrapper for non-object return types."
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond annotations by clarifying that values are normalized 0-100 interest scores, not absolute search volume, which is essential for interpreting results correctly.

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?

Three focused sentences with no filler. The primary purpose is stated first, followed by usage context and a direct pointer to the relevant sibling tool. Every sentence earns its place.

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 the rich input schema, output schema presence, and annotations covering read-only/non-destructive behavior, the description provides all essential decision-making context: what the tool returns, how to interpret the values, and when to use the main alternative. Nothing critical is missing.

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 schema already documents all six parameters thoroughly. The description mostly repeats what the schema conveys ('known keywords', 'timeframe', 0-100 values) without adding new parameter-level meaning, so the baseline of 3 is appropriate.

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 states a specific verb ('Return') and resource ('historical Google Trends interest-over-time points') and explicitly contrasts with get_trending_terms, which is about what is trending now. This makes it easy for an agent to distinguish this tool from the many sibling trend-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear when-to-use guidance: for trajectory and comparisons across a timeframe. It also explicitly names the alternative get_trending_terms for current trending terms, leaving little ambiguity about tool selection.

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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