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 establish readOnly, idempotent, and non-destructive behavior. The description adds valuable context beyond that by explaining that popularity is measured by actual AI assistant fetches, which clarifies the ranking methodology and what makes an article 'popular.'
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 efficient sentences with no filler. The core definition is front-loaded, and the usage examples are immediately useful. Every word 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 list tool with no required parameters, an output schema, and complete annotation coverage, the description is fully sufficient. It explains what the tool returns, how results are ranked, and when to use it.
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 coverage is 100%, with both days and limit fully documented in the input schema. The description does not add parameter-level detail, but it does not need to because the schema already carries that burden.
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 defines the resource as 'articles' and the specific ranking criterion: 'ranked by how often AI assistants have actually fetched them.' This distinguishes it from siblings like list_recent_articles, search, and fetch with a unique, meaningful scope.
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 gives explicit user-intent examples: 'what is your most popular article', 'top reads', 'recommend what to read.' It does not explicitly exclude alternatives or name sibling tools, but the use cases are clear enough to guide selection.
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` both perform keyword search and overlap for article content, while `fetch` and `get_article` both retrieve full text via different identifiers. `list_recent_articles` and `popular_content` are more distinct, but the overall surface has several boundary cases that could mislead an agent.
Names mix bare verbs (`fetch`, `search`), verb-noun forms (`get_article`, `list_recent_articles`, `search_articles`), and a noun phrase (`popular_content`). There is no consistent convention across the set, though the verbs used are generally understandable.
Six tools is within a reasonable range for a content gateway, and each area—search, retrieval, recent, popular—is represented. However, the duplicate search and fetch functionality means the set could be trimmed to four tools without losing much capability.
The server covers the main content-consumption workflow: discover via search/recent/popular, then retrieve full text. There are minor gaps (e.g., no explicit article-by-ID endpoint other than `fetch`, no listing beyond recent/popular), but agents can usually work around them.