structured_data_repair

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

With no annotations, the description fully discloses behavior: fetches page, detects schema, returns ready-to-paste templates, pre-fills from metadata, and flags manual fields. It does not mention any side effects or limitations, but covers key behavioral aspects.

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 concise (5 sentences) with no wasted words. The main purpose is stated first, followed by process details, examples, and output format. Efficient and front-loaded.

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 a simple input and no output schema, the description covers everything: input, process, output (templates), and example triggers. It is fully sufficient for an agent to understand and use the tool.

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?

The single parameter 'url' is described with more detail than the schema alone: 'The page is fetched and its content signals are used to suggest schema types.' This adds value beyond the schema's basic description.

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 action ('Suggest missing JSON-LD additions') and the resource ('for a URL'). It distinguishes from sibling tools like schema_audit by focusing on adding missing types rather than auditing existing ones.

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 clear context on when to use (when missing JSON-LD is suspected) and lists example scenarios (BlogPosting, FAQPage, etc.). However, it does not explicitly state when not to use or directly compare to alternatives, though the sibling list is provided.

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