Extract entities

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

Beyond the readOnly and idempotent annotations, the description adds meaningful behavioral details: results are de-duplicated and returned in the order they first appear. This helps an agent understand output characteristics without needing to invoke the tool.

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 concise sentences front-load the core purpose and follow with the key behavioral detail. Every word earns its place; there is no redundant or filler content.

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 tool with a single simple parameter, full schema coverage, an output schema, and read-only/idempotent annotations, the description is complete. It states the input, the entity categories, deduplication, and ordering, leaving no critical gap.

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 coverage is 100%, with the text parameter already described as 'The text to scan.' The description adds no parameter-specific meaning beyond what the schema provides, so the baseline score of 3 applies.

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 uses a specific verb ('Pull structured items out of free text') and enumerates the exact entity types (email addresses, URLs, #hashtags, @mentions, phone numbers, standalone numbers). This clearly distinguishes the tool from siblings like extract_keywords, since the output categories are explicit and structured.

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 provides clear context for when to use the tool: whenever structured entities need to be pulled from free text. It does not explicitly name alternatives or exclusions, but the enumerated entity types make the applicable use case obvious.

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