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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly states what gets extracted, but it does not disclose the output format, whether all occurrences are returned, how matches are structured, or edge-case behavior for malformed text. Some transparency is present, but significant behavioral details are missing.

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 one short, front-loaded sentence that uses no filler. Every word adds meaning by naming the tool's action and the exact signal types extracted.

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?

The tool is simple in terms of inputs, and the description makes invocation clear. However, with no output schema and no return-value description, an agent is left to guess what the extraction result looks like. That is a meaningful completeness gap for a tool whose entire value is the structured output it produces.

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 schema only defines text as a min-length string with no description, while the description clarifies that text is the source from which signals are extracted. For a single obvious parameter, this adequately compensates for the 0% schema description coverage.

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 a specific verb ('extract') and resource ('text') and enumerates exactly which signals are produced: URLs, emails, money amounts, and ISO dates. This is specific enough to distinguish the tool from siblings like entity_normalize, json_repair, and network_capabilities, which handle different concerns.

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 description implies the tool should be used when a caller needs these signal types pulled out of text, but it never states when not to use it or explicitly contrasts it with alternatives. Usage context is only implicit through the listed extraction targets.

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