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Find new topic ideas

refresh_topic_ideas
Destructive

Use this when someone asks for new blog topic ideas from Reddit or RSS feeds. Returns counts and up to 5 of the saved ideas; it fetches the sources, scores candidates with paid AI and saves those that pass. Not for ideas already saved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blogIdYesBlog to find ideas for
sourcesNoSubreddits and RSS feeds for this run only; the blog's saved sources when absent
audienceNoWho the blog writes for; defaults to the blog's context
minScoreNoLowest AI score, 0 to 10, an idea needs to be saved; defaults to 5
sinceHoursNoHow far back to read, in hours, from 1 to 8,760; defaults to 168 (7 days)
perSourceCapNoMost candidates to score from each source, from 1 to 100; no cap by default
limitPerSourceNoItems to read from each source, from 1 to 100; defaults to 25

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
savedYes
statusYes
fetchedYes
topIdeasYes
newCandidatesYes
sourceErrorCountYes
duplicatesSkippedYes
topIdeasTruncatedYes
alreadyPublishedSkippedYes
sourceErrorDetailsSuppressedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changed
    • changedInput schema / properties / audience / description
      Previous value: -"Audience description. Defaults to the blog `context` field if omitted."New value: +"Who the blog writes for; defaults to the blog's context"
    • changedInput schema / properties / blogId / description
      Previous value: -"Target blog id"New value: +"Blog to find ideas for"
    • changedInput schema / properties / limitPerSource / description
      Previous value: -"Max items fetched per source (default 25)"New value: +"Items to read from each source, from 1 to 100; defaults to 25"
    • changedInput schema / properties / minScore / description
      Previous value: -"Minimum LLM score (0-10) to keep a candidate (default 5)"New value: +"Lowest AI score, 0 to 10, an idea needs to be saved; defaults to 5"
    • changedInput schema / properties / perSourceCap / description
      Previous value: -"Cap on scored candidates per source (default: no cap)"New value: +"Most candidates to score from each source, from 1 to 100; no cap by default"
    • changedInput schema / properties / sinceHours / description
      Previous value: -"Lookback window in hours (default 168 = 7 days)"New value: +"How far back to read, in hours, from 1 to 8,760; defaults to 168 (7 days)"
    • changedInput schema / properties / sources / description
      Previous value: -"Override the blog topicSources. If omitted, the blog `topicSources` field is used."New value: +"Subreddits and RSS feeds for this run only; the blog's saved sources when absent"
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare destructiveHint, openWorldHint, readOnlyHint=false and idempotentHint=false. The description adds genuinely new context beyond those flags: external fetching, paid-AI scoring cost, and that only ideas passing a threshold are persisted. It does not address idempotency or repeat-run behavior, which is the one gap.

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 tight sentences with the trigger condition front-loaded, followed by mechanism and exclusion. The return-shape clause ('counts and up to 5 of the saved ideas') is slightly redundant with the output schema, but costs little.

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?

With 7 parameters, nested sources object, an output schema, and rich annotations, the description covers the trigger, the exclusion, and the write/cost behavior an agent must know before calling. Only the non-idempotent re-run consequences are left implicit and are partly covered by annotations.

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 defaults, ranges, and the per-run vs saved-sources distinction are all documented in the schema. The description adds nothing about parameter semantics beyond noting the save threshold implicitly ('those that pass'), so baseline 3 applies.

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 action (find/fetch new topic ideas) with named sources (Reddit or RSS feeds) and the pipeline it runs (fetch, AI-score, save passing candidates). The closing 'Not for ideas already saved' separates it from list_topic_ideas without the agent 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 Guidelines4/5

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

Gives a clear trigger condition ('when someone asks for new blog topic ideas from Reddit or RSS feeds') and an explicit exclusion ('Not for ideas already saved'). It does not name the sibling to use instead (list_topic_ideas), so routing requires a small 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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