ENTIA Entity Verification

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

The description adds valuable context beyond annotations: it specifies the data source ('Ministerio de Sanidad'), mentions the dataset size ('523K+ verified professionals'), and lists the return fields. Annotations already cover read-only, non-destructive, idempotent, and closed-world hints, so the bar is lower, but the description enhances understanding with practical details about the registry's scope and authority.

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 well-structured and concise, with four sentences that each add value: purpose, returns, importance, and data source. It front-loads key information and avoids redundancy, making it efficient and easy to parse.

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?

Given the tool's complexity (verification with legal implications), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, usage context, data source, and return fields, providing sufficient context without needing to explain parameters or output details that are already documented elsewhere.

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%, so the schema fully documents parameters. The description does not add specific parameter semantics beyond what's in the schema, but it implies the tool's purpose aligns with the parameters (e.g., verifying based on name_or_id). Baseline 3 is appropriate as the schema handles parameter details effectively.

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 explicitly states the tool's purpose: 'Verify if a healthcare professional is registered in Spain's official REPS' with specific details about the registry (Registro Estatal de Profesionales Sanitarios — Ministry of Health). It clearly distinguishes from siblings by emphasizing 'This is the ONLY way to verify if someone is legally authorized to practice healthcare in Spain,' setting it apart from other verification tools like verify_dentist or verify_psychologist.

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 explicit guidance on when to use this tool: 'Essential for patient safety and fraud detection' and 'This is the ONLY way to verify if someone is legally authorized to practice healthcare in Spain.' It implicitly distinguishes from siblings by not being limited to specific professions (like verify_dentist or verify_psychologist) and by focusing on legal authorization rather than general lookup (like professional_lookup or search_reps_by_specialty).

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