ENTIA Entity Verification

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

Annotations already provide key behavioral hints (read-only, non-destructive, idempotent, open-world), but the description adds valuable context beyond this. It discloses the parallel search across multiple sources, the return of a trust score and verification status, and the inclusion of BORME history if available. This enriches understanding of the tool's behavior without contradicting annotations.

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 efficiently structured in three sentences: the first states the purpose, the second details the search process, and the third lists return values. Each sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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, rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers the purpose, search methodology, and return values, providing sufficient context for an agent to understand when and how to use the tool effectively without needing to explain output details redundantly.

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?

With 100% schema description coverage, the input schema fully documents the 'query' and 'country' parameters. The description adds minimal semantic value by mentioning query types (CIF, VAT, etc.) and auto-detection for country, but this largely overlaps with schema details. The baseline score of 3 is appropriate as the schema does the heavy lifting.

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: 'Look up a business entity in ENTIA's verified registry of 5.5M entities across 34 countries.' It specifies the verb ('look up'), resource ('business entity'), and scope ('ENTIA's verified registry'), distinguishing it from sibling tools like 'search_entities' or 'verify_vat' by emphasizing comprehensive cross-referencing across multiple authoritative sources.

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 usage by detailing the sources searched (e.g., BigQuery, BORME, VIES) and the types of queries accepted (CIF, VAT, LEI, company name). However, it does not explicitly state when to use this tool versus alternatives like 'search_entities' or 'verify_vat', nor does it provide exclusions or prerequisites for usage.

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