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 declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds non-obvious behavioral context: popularity is based on AI assistants actually fetching the articles, not generic view counts or editorial picks.
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, front-loaded with the definition and ranking mechanism, followed by concrete query examples. Every sentence earns its place and there is no filler.
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 described parameters, an output schema, and safety annotations, the description provides everything needed: purpose, ranking basis, and typical user queries. Nothing important is missing.
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% for both parameters (days and limit), with defaults and bounds already documented. The description adds no parameter-specific meaning, so it meets the baseline for schema-covered parameters.
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 names a specific resource ('articles') and a specific ranking signal ('how often AI assistants have actually fetched them'). The quoted use cases, such as 'what is your most popular article' and 'top reads', make it easy to distinguish from sibling tools like list_recent_articles or search_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?
It explicitly states when to use the tool: 'Use for "what is your most popular article", "top reads", or to recommend what to read.' This is clear invocation context, though it does not mention when not to use it or name alternative sibling tools.
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.
fetch and get_article both retrieve full text and are differentiated only by identifier type (id versus slug), while search and search_articles both perform keyword searches with different result shapes. Despite helpful descriptions, the boundaries between these pairs are unclear and likely to cause misselection.
Most names use snake_case verb phrases like get_article and list_recent_articles, but popular_content is a noun phrase and fetch/search are bare verbs with no object. The naming is understandable but mixes conventions.
Six tools is a well-scoped set for a read-only content retrieval gateway. Each tool has a clear place: searching, fetching, listing recent, and surfacing popular content.
The tool surface covers the core content lifecycle: search, retrieval, recent articles, and popular content. Minor gaps like pagination or filtering for search and recent listings exist, but agents can work around them.