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trendzeist-mcp

trendzeist-mcp

by phalkmin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

interest_over_time

Retrieve Google Trends search-interest time series for 1-5 keywords, with per-keyword summaries and recent direction, to decide which topics to write about now.

Instructions

Search-interest time series (0-100) for 1-5 keywords, with per-keyword summary (mean, latest, peak, direction over the whole window, direction_now = recent slope, growth_3m/growth_12m % and a plain-English insight). Use direction_now to decide whether to write about a topic now; direction can be 'rising' or 'new' for a topic that peaked months ago. A sustained step in a series is reported as summary..level_shift and in warnings (shared by several keywords = likely a Google sampling change): growth figures spanning that week overstate the change. Long series are downsampled to ~60 points. gprop: '' (web), 'news', 'youtube' (authority channels answer engines cite), 'images', 'froogle'.

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": {
      "keywords": "<keywords>"
    },
    "name": "interest_over_time"
  }
}

Input Schema

NameRequiredDescriptionDefault
geoNo
gpropNo
categoryNo
keywordsYes
timeframeNotoday 12-m

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations supplied, the description carries the full burden and does well: it discloses downsampling to ~60 points, the level_shift detection, warnings that a shared level shift implies a Google sampling change, and that growth figures spanning that week overstate the change. It omits rate limits, quota/error behavior, and whether requests are cached, which are the notable remaining gaps for a read tool.

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 lead sentence front-loads the core purpose and output shape, and the caveats follow in descending importance. It is dense and slightly run-on in the middle section on level_shift and sampling warnings, but every clause carries information rather than filler.

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

Completeness3/5

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

An output schema exists, so the field-by-field summary is value-add rather than required, and the behavioral caveats are strong. The definition is nevertheless incomplete for a 5-parameter tool: geographic scoping, category filtering, and timeframe formatting are unaddressed, and those materially change results.

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 0% across 5 parameters, so the description must compensate. It explains gprop's value set ('news', 'youtube', 'images', 'froogle') and the 1-5 keyword bound, but geo, category, and timeframe are never addressed, leaving three parameters with no semantics anywhere.

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?

Opens with a precise verb-and-resource pair: 'Search-interest time series (0-100) for 1-5 keywords', including the measurement scale and a hard keyword cap. That framing distinguishes it from siblings such as trend_history, compare_keywords, and interest_by_region without needing to name them.

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

Usage Guidelines3/5

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

It offers interpretive guidance ('Use direction_now to decide whether to write about a topic *now*') including a specific pitfall: direction can be 'rising' or 'new' for a topic that peaked months ago. However, it never states when to pick this over compare_keywords or trend_history, and gives no exclusions or prerequisites.

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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