extract_structured

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

The description specifies the types of structured data extracted (JSON-LD, OpenGraph, Twitter cards), which adds behavioral context beyond the annotations (readOnlyHint, idempotentHint, openWorldHint). However, it does not discuss error cases or behavior for missing data.

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 a single sentence with no wasted words. It is front-loaded with the action and resource, making it efficient and easy to parse.

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 two parameters and an existing output schema, the description is mostly adequate. It covers what the tool extracts but omits details about the 'format' parameter and return structure, which are partially addressed by the output schema.

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 0%, meaning the description does not explain the two parameters ('url' and 'format'). Although the schema is self-explanatory for 'url', the 'format' enum's impact on output is not clarified, missing an opportunity to add value.

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?

Description clearly states the verb 'Pull' and resource 'JSON-LD, OpenGraph, Twitter cards from a web page', making the tool's purpose unambiguous. It distinguishes from siblings like 'fetch' (raw HTML) or 'read_doc' (document processing).

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?

No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives (e.g., 'fetch', 'image_search'), nor does it mention when not to use it or any prerequisites.

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