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TrendHub

by io.github.Zachary-1012
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professional_intelligence

Professional Intelligence v3：品牌、公司、商业体、产品、Campaign 的 Entity-first 决策入口。先主动检索主体相关公开证据，再结合生命周期、Source Reliability、Evidence Truth State、异常、6/24/48/72h验证预测、媒体/受众、机会风险与高管报告。品牌/实体研究优先用本工具；纯话题趋势研究才用 analyze_topic；只看当前榜单才用 get_trending。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoEntity-first 搜索趋势地区，默认 CN；如 CN/HK/US
reportNo附带高管报告格式，默认 json
keywordYes要研究的品牌、公司、商业体、产品、Campaign 或商业主体，例如 广州太古汇 / LV / 小米 / Tesla
refreshNo是否先刷新当前公开数据，默认 true
platformsNo可选平台调用名，逗号分隔；留空时由专业 Source Planner 自动选择零配置核心源
timeframeNoEntity-first 搜索趋势时间窗，默认 today 3-m
verticalsNo可选行业/场景，逗号分隔：fashion-luxury,beauty,business-corporate,technology,automotive,finance-markets,marketing-advertising,retail-commerce,culture-entertainment
days_aheadNo未来节点窗口，默认60天

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / days_ahead
      Added value: +{
+  "description": "未来节点窗口，默认60天",
+  "maximum": 365,
+  "minimum": 7,
+  "type": "number"
+}
    • addedInput schema / properties / geo
      Added value: +{
+  "description": "Entity-first 搜索趋势地区，默认 CN；如 CN/HK/US",
+  "type": "string"
+}
    • changedInput schema / properties / keyword / description
      Previous value: -"要研究的话题、品牌、公司或关键词，例如 LV / 小米 / Tesla"New value: +"要研究的品牌、公司、商业体、产品、Campaign 或商业主体，例如 广州太古汇 / LV / 小米 / Tesla"
    • addedInput schema / properties / timeframe
      Added value: +{
+  "description": "Entity-first 搜索趋势时间窗，默认 today 3-m",
+  "type": "string"
+}
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the annotations, the description discloses a concrete methodology: proactive retrieval of public evidence, lifecycle analysis, source reliability, evidence truth state, anomaly detection, validation predictions, media/audience analysis, opportunity/risk assessment, and executive reporting. It does not contradict annotations; the refresh behavior aligns with readOnlyHint=false.

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 description is front-loaded with the tool's identity and purpose, and the routing guidance is compressed into a clear final clause. The capability list is dense but each item adds scope information. It could be trimmed slightly, but no sentence is wasted.

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

Completeness4/5

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

For an 8-parameter tool with no output schema, the description gives enough operational context: what kind of entity to research, what dimensions are analyzed, and which sibling tools to use instead. The schema covers parameter details, and the description covers strategy and differentiation, so an agent can call it correctly with minimal ambiguity.

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 parameters are already individually documented. The description adds some high-level context (e.g., executive report, evidence retrieval) but does not clarify specific parameter syntax, defaults, or interactions beyond what the schema provides. Baseline 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 clearly states this is an Entity-first decision entry for brands, companies, business entities, products, and campaigns, and explicitly contrasts it with analyze_topic and get_trending. An agent can immediately identify what resource this tool operates on and how it differs from its siblings.

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?

It gives direct routing guidance: brand/entity research should use this tool, pure topic trend research should use analyze_topic, and current rankings should use get_trending. This is explicit when-to-use versus alternatives guidance, leaving little to inference.

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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