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

The description adds valuable context beyond the read-only annotations: it discloses that results include byte offsets and normalized values, and it states the x402 pricing model and that failed calls are free. It is less clear about result cardinality (single vs. multiple matches), input size limits, or overlap handling.

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 definition is compact and front-loaded: the extraction scope and supported types come first, followed by pricing. Every sentence carries useful information with no filler.

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?

With no output schema, the description gives only a high-level account of return values (fields, offsets, normalized values) and does not describe the input mechanism despite the empty parameter schema. It also omits the extract_custom alternative, which is a meaningful completeness gap for this sibling set.

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?

There are zero input parameters, so the baseline is 4. The description does not need to explain parameters, though it also does not clarify how the source text is supplied to a tool with an empty schema.

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 a specific verb and resource: extracting structured fields from unstructured text. It enumerates the entity types it supports, which makes its purpose concrete. However, it does not explicitly distinguish itself from the sibling extract_custom tool, which is the most important adjacent alternative.

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?

The enumerated entity types imply when the tool is applicable, but there is no explicit when-to-use or when-not-to-use guidance. In particular, it never says to use extract_custom instead for custom patterns, leaving the agent to infer the boundary.

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