ENTIA Entity Verification

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

Annotations already provide readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe, non-destructive read operation. The description adds valuable context beyond annotations by specifying the data source ('40.3M mercantile acts (2009-2026)') and the types of officers covered ('administradores, consejeros, apoderados, liquidadores, auditores'), enhancing behavioral understanding.

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 front-loaded with the core purpose, followed by essential use cases and key details, all in four concise sentences with zero waste. Each sentence adds value without redundancy.

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 (tracking officer changes with filtering), rich annotations (read-only, non-destructive, idempotent), and the presence of an output schema, the description is complete. It covers purpose, use cases, data scope, and source, providing sufficient context for an agent to understand when and how to use this tool effectively.

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 input schema already fully documents the three parameters (company_or_person, change_type, days). The description does not add any parameter-specific details beyond what the schema provides, such as examples or edge cases, meeting the baseline for high schema 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 clearly states the tool's purpose with specific verbs ('track officer appointments and removals') and resources ('Spanish companies via BORME'), and it distinguishes this from siblings by focusing on officer changes rather than entity lookup, new constitutions, or other searches.

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 explicitly states when to use this tool ('Essential for: due diligence, KYC/KYB, detecting corporate governance changes, identifying when key people join or leave companies'), providing clear context and use cases that differentiate it from sibling tools like borme_lookup or entity_lookup.

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