schema_audit

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

No annotations provided, so description carries full burden. It describes parsing, validation logic per @type, flagging missing fields/malformed JSON, and returns issues list and verdict. Does not mention read-only nature or safety, but validation implies non-destructive behavior.

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?

Three concise sentences that front-load the main purpose, then detail specific checks, and end with usage recommendation. No wasted words.

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?

Describes input (URL), process (parsing and validation), output (issues list and verdict), and provides examples of checks. Since no output schema exists, this explains return values adequately.

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 'url' with a schema description that matches the description. Schema coverage is 100%, so the description adds minimal extra meaning beyond the schema. 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 it performs deep schema.org validation for a URL, parsing all JSON-LD and microdata, and returns issues/verdict. It distinguishes from sibling tools by mentioning it fixes bugs that predict_citation can't explain.

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?

Explicitly says to use when predict_citation flags bugs but can't explain them, providing clear use case context. Does not explicitly state when not to use, but sibling differentiation implies alternatives.

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