search_articlesSearch this publication's articles by keyword. Returns the best-matching titles, dates, canonical URLs, and short summaries.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 5, max 20) | |
| query | Yes | Search keywords |
| Name | Required | Description | Default |
|---|---|---|---|
| total | No | ||
| articles | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive behavior. The description adds useful behavioral detail by stating that it returns 'best-matching' results with titles, dates, canonical URLs, and short summaries. This enriches what the agent can expect beyond the annotations.
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?
The description is a single clear sentence that front-loads the core purpose and result summary. No redundant phrases or filler; every word contributes.
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 two-parameter search tool with a rich output schema and safety annotations, the description is complete. It communicates scope, search method, and result contents without needing extra explanation.
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 both 'query' and 'limit' are already documented. The description's mention of 'by keyword' aligns with 'query' but adds no additional parameter semantics beyond the schema's descriptions.
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 states a specific verb ('Search'), a specific resource ('this publication's articles'), and the method ('by keyword'), and outlines the returned fields. It effectively distinguishes itself from siblings like get_article and list_recent_articles by focusing on keyword-based search with summaries and URLs.
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 implies the tool is for keyword-based article searches, but does not explicitly say when to prefer it over the sibling 'search' or 'list_recent_articles'. There is no exclusion guidance, but the 'by keyword' phrasing gives some contextual direction.
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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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.