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?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds a valuable behavioral nuance beyond those annotations: the ranking is based specifically on AI assistant fetch frequency, not general human pageviews. This helps the agent understand what kind of 'popularity' the tool measures.
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 sentence defines what the tool returns and its ranking logic; the second provides concrete usage examples. Every word earns its place and the most important information is front-loaded.
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 list tool with two fully documented optional parameters, annotations covering safety, and an output schema present, this description is complete. It explains the ranking source and gives practical invocation cues, so an agent has everything needed to call 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?
Input schema coverage is 100% and both parameters (days, limit) have descriptive documentation. The tool description does not add additional parameter-level meaning beyond the schema, so the baseline score of 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 resource (popular articles) and the unique ranking criterion (how often AI assistants have fetched them). It lacks an explicit verb like 'list' or 'retrieve,' but the 'Use for' phrasing makes the retrieval purpose unambiguous and distinguishes it from sibling tools like search and 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 gives concrete, natural-language use cases ("what is your most popular article", "top reads", recommending what to read), which helps an agent know when to invoke this tool. It does not explicitly state when not to use it or name alternatives, but the usage guidance is clear and actionable.
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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Each tool has a clear primary role, but search and search_articles overlap for keyword queries, and fetch and get_article both retrieve full text by different identifiers. Descriptions help distinguish them, but an agent could still select the wrong tool without careful attention.
Most tools use verb-led names like fetch, get_article, list_recent_articles, and search, but popular_content breaks the pattern as a noun phrase. The pairings of fetch/get_article and search/search_articles also create inconsistent specificity across similar operations.
Six tools is a well-scoped set for a content retrieval gateway, covering discovery and retrieval without padding. Each tool earns its place and supports a coherent read-only workflow.
The surface covers searching, browsing recent and popular content, and fetching full text through two paths. Minor gaps remain, such as no direct URL-based fetch and no explicit way to get full text from search_articles results without extracting a slug, but agents can work around these.