ENTIA Entity Verification

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

Annotations already indicate read-only, non-destructive, idempotent, and closed-world behavior. The description adds valuable context beyond this: it discloses data source limitations ('coverage is partial'), specifies the return fields (colegiado number, name, colegio, province, specialties), and identifies the data source ('Colegio Oficial de Psicologos de Espana'), enhancing the agent's understanding of reliability and output.

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 important notes and return details in a logical flow. Every sentence adds value: the first defines the tool, the second covers limitations and alternatives, and the third specifies returns and data source, with no wasted words.

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 moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema, the description is complete. It covers purpose, usage guidelines, data limitations, alternatives, return fields, and data source, providing all necessary context for effective agent use without redundancy.

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%, providing clear documentation for both parameters. The description does not add significant semantic details beyond the schema, such as formatting examples or search behavior nuances. However, it implies the tool's purpose aligns with the parameters, maintaining the baseline score 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 specific action ('verify if a psychologist holds a valid colegiado number'), identifies the resource ('Spain's official COP registry'), and distinguishes from a sibling tool by explicitly naming 'search_reps_by_specialty' as an alternative for broader results. This provides precise differentiation and avoids tautology.

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 provides when to use this tool (for COP registry verification) and when to use an alternative ('search_reps_by_specialty' for broader results from Ministerio de Sanidad). It also notes coverage limitations ('partial' due to re-harvesting), offering clear contextual guidance for tool selection.

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