audit_schema

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

Annotations already declare readOnly, idempotent and non-destructive behavior, so safety is covered. The description adds real value beyond that: it discloses the parsing method (every JSON-LD block and microdata node), the validation rules applied per @type, and that malformed JSON-LD is flagged. It does not discuss cost, rate limits, or failure modes.

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 dense sentences, front-loaded with the verb and resource, and the inline @type examples earn their place by showing the depth of validation. Slightly run-on, but there is no filler.

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?

An output schema exists and the description still summarizes the return shape (issues list plus valid/invalid verdict), so return values are covered. Annotations cover the safety profile. The main omission is routing logic versus the very similar audit_structured_data sibling.

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?

Only one parameter and schema description coverage is 100%, so the schema already defines the url fully. The description adds only that both JSON-LD and microdata at that URL are validated, which is scope rather than parameter syntax. 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?

States a specific verb (validate) and resource (schema.org structured data) and enumerates exactly what is parsed and checked (JSON-LD blocks, microdata nodes, required fields per @type). However, it never distinguishes itself from the near-identical sibling audit_structured_data, so an agent cannot tell the two apart from the description alone.

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?

Gives a concrete trigger: use it to diagnose structured-data bugs that predict_citation flags but cannot explain. That is a clear when-to-use context. It does not state when to prefer audit_structured_data instead, nor any preconditions (e.g., page must be reachable).

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