trendzeist-mcp

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discover_topics

Turn 1-5 seed keywords into ranked blog topics using 12-month Google Trends data, related queries, questions, and coverage gaps versus your site.

Instructions

One-shot topic discovery for blog ideation. For 1-5 seed keywords, pulls 12-month trend context (direction, direction_now, growth_3m) plus rising/top related queries, de-duplicates and ranks candidates as breakout > rising > evergreen, each tagged with a title angle and an explainable score. questions collects question-shaped searches across seeds; suspect lists spam-like rising queries kept out of the ranking. site_queries = queries the site already ranks for (fill from a Search Console MCP or page titles): each topic then gets coverage covered / partial / gap, and exclude_covered drops the covered ones. Partial failures are reported per seed in errors instead of failing the call. First call of a session: start with 1-2 seeds to avoid a 429.

Input Schema

TableJSON Schema

Name	Required	Default
`geo`	No
`gprop`	No
`category`	No
`timeframe`	No	today 3-m
`max_per_seed`	No
`site_queries`	No
`seed_keywords`	Yes
`exclude_covered`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.5.0Oct 17, 2026

addedInput schema / properties / exclude_covered

Added value: +{
+  "default": false,
+  "title": "Exclude Covered",
+  "type": "boolean"
+}

addedInput schema / properties / site_queries

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Site Queries"
+}

First observedv0.2.1Sep 10, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations provided, the description carries the behavioral burden and does well: it discloses deduplication, ranking tiers, partial-failure reporting via errors, rate-limit caution, and coverage tagging. It still omits explicit safety/auth or read-only guarantees, but the behavioral picture is strong.

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 dense but front-loaded and nearly every sentence adds concrete information about behavior or parameters. It is a single long paragraph rather than structured bullets, which slightly reduces readability, but it avoids empty filler.

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?

Given an output schema, the description need not detail return values, yet it still explains output fields and failure modes well. The main gap is that several input parameters remain undocumented despite 0% schema coverage, so an agent lacks full parameter context.

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%, so the description must compensate for 8 parameters. It explains seed_keywords (1-5 seeds), site_queries, exclude_covered, and the resulting coverage labels, but leaves geo, gprop, category, timeframe, and max_per_seed unexplained, providing only partial compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'One-shot topic discovery for blog ideation,' and clarifies it pulls trend context plus related queries and ranks candidates. It does not, however, explicitly distinguish itself from siblings such as related_queries or trending_now, so it falls short of the top score.

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?

It gives clear usage context ('for blog ideation'), guidance to start with 1-2 seeds on the first call to avoid 429s, and explains when to supply site_queries. It does not state when-not to use this tool or name alternative sibling tools, so it is not fully explicit.

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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