JSON-LD / structured data extractor

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

With no annotations, the description carries the behavioral burden. It usefully discloses parsing/indexing behavior, optional type filtering, microdata fallback, and the $0.01/USDC cost. It does not mention failure behavior, response structure, rate limits, or authentication expectations, but it is not misleading or contradictory.

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 compact and front-loaded with the core behavior, followed by a one-sentence use case and a brief cost note. Every sentence earns its place; no filler or redundant restating of the tool name.

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 two-parameter extraction tool with no output schema, the description gives enough context to understand the scope, filter behavior, fallback, and cost. It could add a note about the returned data shape, but the low parameter complexity and clear use cases make it sufficiently complete.

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 description coverage is 100%, so the schema already fully documents both url and type. The description's 'optional type filter' adds no new semantic detail beyond what the schema provides, so the baseline of 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 states exactly what the tool does: it extracts all JSON-LD blocks from a page, parses them, indexes them by schema.org type, supports an optional type filter, and falls back to microdata. This clearly distinguishes it from sibling tools like farm_metadata and farm_links by naming JSON-LD/structured data as the specific resource.

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?

It gives clear use cases: 'read product, price, article or event data without writing a scraper.' However, it does not explicitly say when not to use this tool or name an alternative for other metadata/extraction needs, so it stops short of a full when/when-not comparison.

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