Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description discloses key behaviors: it returns multi-page synthesized answers with citations, auto-crawls for unindexed sites, and has a ~60s latency. It also indirectly indicates it is more robust than web_fetch for JS-rendered content. No annotations are present, so this is the primary transparency source.

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?

Four sentences: opening purpose, numbered use-case list, and unindexed-site note. Every clause is informative, no padding, and the structure guides the reader from general to specific.

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?

Given the tool's moderate complexity (3 params, no output schema), the description covers the core function, return value (cited answers), and edge case (auto-crawling). It does not need to explain return structure further.

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 covers all three parameters (domain, query, url) with clear descriptions, and the description does not add parameter-specific guidance beyond what's already in the schema. The level of detail in the schema makes up for the absence of extra param semantics.

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 opens with a clear action ('Query any website's documentation') and outcome ('cited, multi-page answers in natural language'). It distinguishes from siblings by explicitly contrasting with web_fetch (for JS-rendered sites) and referencing submit_site as unnecessary when auto-crawling.

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?

Provides four explicit use cases with examples (synthesis, JS-rendered sites, citations, speed) and notes when not to use it (for sites not indexed, it handles auto-crawling, so no separate submit_site call). This is strong guidance for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.