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Suggest article topics

suggest_topics
Read-only

Generates topic suggestions for a site's next articles, using the same logic as the dashboard's topic generator — biased toward an experiment/case-study angle for a share of the results, and informed by the site's differentiators if set, rather than generic keyword-chasing titles. Fast (a few seconds, not a background job) — use this before generate_article or schedule_article instead of inventing topics yourself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoHow many topic suggestions to generate
siteIdYesTarget site id (from list_sites) — determines language, tone, SEO tags and differentiators used
creativityNo0 = safe/proven angles, 100 = more experimental angles

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), so the description's value-add is the latency disclosure ('fast — a few seconds, not a background job') and the output bias (experiment/case-study angle, informed by site differentiators). It does not state whether results are cached or rate-limited, which keeps it short of a 5.

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?

Two sentences, front-loaded with purpose then usage, and the latency caveat is placed at the point of decision. The middle clause about the experiment/case-study bias is slightly dense but earns its place by describing output character.

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?

Usage, latency and output character are well covered for a read-only generation tool, but there is no output schema and the description never says what a 'topic suggestion' actually is (title only, title plus rationale, structured object). That is the one remaining gap.

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 siteId, count and creativity including ranges and defaults. The description adds no parameter-level syntax or format detail beyond what the schema provides, making the baseline 3 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?

States a specific verb and resource ('Generates topic suggestions for a site's next articles') and immediately frames the output as distinct from the sibling content-creation tools. The mention of the dashboard's topic generator and the experiment/case-study bias lets an agent tell this apart from generate_article without opening either schema.

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?

Explicitly says to use this 'before generate_article or schedule_article instead of inventing topics yourself', naming both the alternative tools and the condition that selects this one. No inference required.

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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