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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context such as field-level confidence scores and GDPR/CCPA suitability, but it does not address limitations, and its reference to 'documents' is not reconciled with the schema's plain-text-only constraint.

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 two sentences, front-loads the core extraction purpose, and adds compliance/output context without waste. Every phrase contributes to the tool's scope or output behavior.

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 one-parameter read-only tool with rich annotations and full schema coverage, the description is adequate but incomplete. It mentions confidence scores but does not describe return shape, and it lacks explicit routing versus sibling extraction tools or clarity on the plain-text-only input.

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 100%, and the single text parameter is fully documented in the schema. The description adds no syntax or format detail beyond the schema and could mildly mislead by mentioning 'documents' despite the text-only restriction, so baseline 3 is appropriate.

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?

States a specific verb and resource: it identifies and structures contact fields such as names, emails, phone numbers, LinkedIn URLs, and addresses. This clearly separates it from generic extraction siblings, though it does not explicitly differentiate itself from tools like extract_resume_data or tag_entities.

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 gives no explicit guidance on when to use this tool instead of alternatives such as extract_resume_data, extract_job_data, or tag_entities. It implies a contact-extraction use case, but provides no when/when-not conditions or routing cues.

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