popular_contentThe most-read / most-requested articles, ranked by how often AI assistants have actually fetched them. Use for "what is your most popular article", "top reads", or to recommend what to read.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back window in days (default 30, max 365) | |
| limit | No | How many to return (default 10, max 25) |
| Name | Required | Description | Default |
|---|---|---|---|
| articles | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds useful context by explaining the popularity ranking is based on actual AI-assistant fetches, but it does not disclose further behavioral details such as pagination behavior or response shape. This is acceptable given the annotations and output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: the first states what the tool returns and how ranking works, the second gives concrete query phrases for when to use it. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with two optional fully-documented parameters, complete annotations, and an output schema, the description is sufficient. Nothing essential is missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with days and limit both fully described including defaults and bounds. The description does not add parameter-level detail, but it does not need to because the schema already carries that information. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as returning most-read or most-requested articles, with a specific ranking criterion: how often AI assistants have actually fetched them. This is a specific verb+resource+ranking definition that makes it easy to distinguish from siblings like search_articles or list_recent_articles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly gives concrete use cases: 'what is your most popular article', 'top reads', or recommending what to read. It does not mention when not to use it or name alternatives, but the clear context is sufficient for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Search and search_articles overlap significantly, both retrieving article matches with slightly different result formats. Fetch and get_article also both return full article text but are distinguished by id versus slug. Descriptions help resolve these overlaps, but an agent could still pick the wrong tool without close attention.
Most tools follow a snake_case verb_noun pattern like get_article and search_articles, but fetch and search are bare verbs, and popular_content is a noun phrase with no verb. The pattern is readable but not fully consistent.
Six tools is a well-scoped set for a publication content gateway: search, retrieval by two identifier types, recent listing, popular content, and article search. Each tool serves a distinct practical need without bloat.
The core content access workflows are covered: search, retrieve full text by search id, retrieve by slug, list recent, and list popular. Minor gaps exist, such as no pagination or filtering, and search_articles results don't explicitly feed into get_article or fetch, but agents can typically work around these.