extract_metadata

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

Annotations already establish read-only, open-world, non-destructive behavior. The description adds what data is targeted and a reliability/cost comparison, but it does not explain pagination/truncation via offset/max_chars or the extraction scope.

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 are front-loaded with the data types and followed by a practical comparison. There is little waste.

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, so the description need not document return values, and it does communicate the main product fields. However, with zero schema parameter descriptions, the omission of offset/max_chars behavior leaves the call semantics incomplete.

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%, and the description never mentions offset or max_chars. The parameter names and defaults give minimal clues, but the description does not compensate for the missing schema documentation.

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 names the specific embedded formats (JSON-LD, OpenGraph, microdata, common meta tags) and a schema.org Product summary, so the resource and output focus are clear. It does not explicitly distinguish itself from the sibling extract_structured, leaving some overlap.

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 offers an implied usage context by saying this is often more reliable and cheaper than CSS scraping on shops. However, it does not state when to choose this over extract_structured or other extraction siblings, nor does it give exclusions.

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