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 and destructiveHint=false. The description adds meaningful behavioral context by clarifying that 'popular' is based on actual AI assistant fetches, not generic page views or sales. This helps the agent interpret results correctly.
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 concise sentences: the first defines the tool's output and ranking basis, the second gives practical invocation examples. Every sentence earns its place and the definition 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?
The tool is simple with two optional parameters, full schema coverage, an output schema, and annotations covering safety. The description fully explains what the tool returns and when to use it, so nothing essential 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%, with both parameters (days and limit) fully documented in the schema. The tool description adds no parameter-specific detail beyond what the schema provides, 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 names a specific resource ('articles') and a specific ranking criterion ('ranked by how often AI assistants have actually fetched them'), which clearly distinguishes it from siblings like best_sellers or list_recent_articles. The verb and scope are clear.
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 explicit use cases: 'what is your most popular article', 'top reads', or recommending what to read. It provides clear context for when to invoke the tool, though it does not explicitly exclude alternatives like best_sellers for product popularity.
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.
Most tools are scoped to either products or articles, but there are overlapping pairs: check_inventory and get_product both return price and variant stock, and fetch and get_article both retrieve full-text content. search, search_products, and search_articles also share similar search intent, though their descriptions help clarify scope.
The set mixes verb+noun tools like list_products, get_article, and search_products with bare verbs like fetch and search, and noun-phrase tools like best_sellers, popular_content, related_products, and store_info. The naming is readable and partially patterned, but not consistently structured.
15 tools is at the upper end of the typical range, covering both a storefront and a publication plus sitewide search. The count is reasonable for the combined scope, though a few tools like check_inventory and get_product overlap enough that the set could be slightly tighter.
The gateway covers catalog browsing, product search, inventory, cart building, store policies, article search/retrieval, recent content, popular content, and general site search. Minor gaps include no paged listing of all articles beyond recent and no direct category-filtered product listing, but core workflows are well supported.