extract_structured_data

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

No annotations are provided, so the description carries full burden. It discloses that the tool extracts and summarizes JSON-LD data and reports parse errors, but it does not explicitly state whether it modifies data (likely read-only), authorization needs, or behavior for pages without JSON-LD. Some behavioral context is present but not comprehensive.

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 two sentences with no fluff. The first sentence front-loads the action and specifics, and the second sentence provides usage guidance. Every sentence adds value.

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 tool with one parameter and no output schema, the description covers what it does and what it returns (types, fields, errors). It could mention error handling for invalid URLs, but overall it is fairly complete given the tool's simplicity.

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% (the single 'url' parameter is described in the schema). The description adds context that the extraction is for JSON-LD structured data but does not significantly augment the parameter's meaning. Baseline 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 clearly states the verb (extract and summarize), the resource (page's JSON-LD structured data), and specifics (types, key fields, parse errors). It also distinguishes from sibling tools like audit_url or extract_page_signals by focusing solely on JSON-LD schema markup.

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 explicitly provides a use case: 'Use when asked whether a page has the schema markup AI answer engines rely on for citations.' While it doesn't mention when not to use or alternatives, the context is clear enough for an AI agent.

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