searchSearch this site's content. Returns matching documents (id, title, url); pass an id to fetch for the full text.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
| Name | Required | Description | Default |
|---|---|---|---|
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context by clarifying that results are documents with id/title/url and that full text is deliberately not returned here—routing the agent to 'fetch' instead. This goes beyond what the annotations state.
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 short sentences, each earning its place. The core action is front-loaded, return behavior is stated, and the follow-up action to 'fetch' is included without unnecessary detail.
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 single-parameter tool with read-only annotations and an output schema, the description is mostly complete: it states what is returned and how to get full text via 'fetch'. The only notable gap is the lack of explicit disambiguation from 'search_articles', but the low complexity keeps this from being a major omission.
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 already documents 'query' as a search query. The description does not add much semantic detail beyond that, but it does frame the query as a search over site content. This matches the baseline for high schema coverage.
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 states a specific action ('Search this site's content') and the resource being searched, and adds what is returned (matching documents with id, title, url). It mentions the relationship to 'fetch', but does not explicitly distinguish itself from the sibling 'search_articles', so sibling differentiation is incomplete.
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 when to use the tool: when you need to search site content. It also gives a useful next-step instruction ('pass an id to fetch for the full text'). However, it provides no explicit when-not-to-use guidance or comparison to the closely related sibling 'search_articles'.
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 overlap significantly, both retrieving article matches with slightly different result formats. Fetch and get_article also both return full article text but are distinguished by id versus slug. Descriptions help resolve these overlaps, but an agent could still pick the wrong tool without close attention.
Most tools follow a snake_case verb_noun pattern like get_article and search_articles, but fetch and search are bare verbs, and popular_content is a noun phrase with no verb. The pattern is readable but not fully consistent.
Six tools is a well-scoped set for a publication content gateway: search, retrieval by two identifier types, recent listing, popular content, and article search. Each tool serves a distinct practical need without bloat.
The core content access workflows are covered: search, retrieve full text by search id, retrieve by slug, list recent, and list popular. Minor gaps exist, such as no pagination or filtering, and search_articles results don't explicitly feed into get_article or fetch, but agents can typically work around these.