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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the ranking signal (AI fetch counts), but doesn't mention return format, caching, or potential staleness. With strong annotations, 3 is appropriate.
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 core purpose and the differentiating ranking basis, followed by example queries. No wasted words.
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?
With an output schema present and full parameter schema coverage, the description is nearly complete. The only minor gap is not specifying whether 'days' is relative to current date or if results include title/URL metadata, but the output schema likely covers return shape. Strong for its complexity.
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%, so the schema fully documents both parameters (days, limit). The description does not add extra meaning about parameters, which is acceptable because the schema carries the burden. Baseline 3 applies.
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?
States a specific verb and resource ('most-read / most-requested articles'), and uniquely clarifies the ranking basis: how often AI assistants have actually fetched them. This distinguishes it from generic search or recent-article tools.
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?
Explicitly gives example queries ('what is your most popular article', 'top reads') and a use case ('recommend what to read'). While it doesn't name an alternative tool, sibling tools like list_recent_articles and search_articles are clearly different by virtue of the popularity ranking context.
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.
The set has clear overlaps: search and search_articles both perform keyword search, while fetch and get_article both retrieve full text using different identifiers. An agent could easily select the wrong tool, especially since the descriptions do not clarify when one search path is preferred over the other.
Tool names are mostly verb-led but not uniform: fetch and search are bare verbs, get_article and search_articles use verb+object, list_recent_articles is verb+adjective+noun, and popular_content is a noun phrase. The naming is readable but lacks a consistent convention.
Six tools is a reasonable size for a content gateway, but two pairs of near-duplicates reduce the effective surface and leave some redundancy. The count itself is not excessive, but the same functionality could be delivered with fewer, more distinct tools.
Core content-retrieval workflows are covered: searching, fetching full articles, viewing recent articles, and accessing popular content. Minor gaps exist, such as category/author browsing or pagination, but they are not critical dead ends for the apparent purpose.