Attention Trends

by dev.workers.shopttyass-85f.attention-trends

Compare Topic Popularity

compare_topic_popularity

Read-onlyIdempotent

Compare the popularity of 2-5 topics (brands, competitors, products, people, keywords) by Wikipedia pageviews. Returns one jointly normalized 0-100 interest-over-time series per topic, share of attention, the most popular and the fastest-rising topic, and per-topic trend direction and spikes. A Google-Trends-style popularity comparison based on Wikipedia reading rather than searches. Suited to competitor analysis, brand tracking and content planning. Paid: $0.01 USDC per call via x402.

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": {
      "topics": "<topics>"
    },
    "name": "compare_topic_popularity"
  }
}

Input Schema

Name	Required	Description
`days`	No	Window length in days, ending yesterday UTC (default 30)
`lang`	No	Wikipedia language code, e.g. 'en', 'fr', 'de', 'ja' (default en)
`topics`	Yes	2-5 comma-separated topics, e.g. 'ChatGPT,Claude,Gemini'

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 22, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, it discloses the return shape (normalized series, share of attention, top and fastest-rising topic, trend direction and spikes), data source semantics (Wikipedia reading, not searches), and the paid x402 pricing — a critical operational fact annotations do not cover.

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?

Front-loads what it does, then returns, positioning, use cases, and pricing in five tight sentences with no filler. Every sentence contributes a distinct fact an agent needs.

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?

With no output schema, the description fully covers return values; with annotations covering safety and a small 3-param schema, nothing operationally important is missing. Pricing and data-source caveats round out a complete picture for a read-only comparison tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so days and lang are already documented with defaults and ranges. The description adds semantic meaning by specifying that 2-5 topics are compared and enumerating the kinds of entities that work (brands, competitors, products, people, keywords), helping an agent choose valid inputs.

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 (compare) and resource (topics' popularity via Wikipedia pageviews), with scope (2-5 topics) and topic types. The 'jointly normalized 0-100 series per topic' and 'compare' framing distinguish it from the single-series sibling wikipedia_pageviews_trend without needing the schema.

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?

Gives clear use-case context ('competitor analysis, brand tracking and content planning') and positions it as a Google-Trends-style alternative, implying when it fits. It does not explicitly name the siblings or state when-not to use it, leaving routing to inference.

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

All Attention Trends tools Inspect

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