ENTIA Entity Verification

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

The annotations already provide readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe, non-destructive, repeatable read operation. The description adds valuable context about the data source ('40.3M mercantile acts, 3.4M unique companies (2009-2026)') and real-time nature, but doesn't disclose additional behavioral traits like rate limits, authentication requirements, or pagination behavior beyond what's in the schema.

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 appropriately sized. It starts with the core purpose, then provides usage context, filtering guidance, and data source information. While the list of common CNAE codes is helpful, it could be slightly more concise. Overall, most sentences earn their place by adding value beyond what's in other fields.

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 that this is a read-only tool with comprehensive annotations, 100% schema coverage, and an output schema (implied by 'Has output schema: true'), the description provides adequate context. It covers purpose, use cases, filtering semantics, and data source credibility. The main gap is the lack of explicit guidance on when to choose this tool versus specific sibling alternatives for related tasks.

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 already documents all four parameters thoroughly. The description adds some semantic context by providing common CNAE code examples (8623 for dental, 6910 for legal, etc.) and mentioning province filtering for 'tracking specific markets,' but doesn't add significant meaning beyond what the schema already provides. The baseline of 3 is appropriate when 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: 'Get newly constituted companies from Spain's BORME in real time.' It specifies the exact action ('Get'), resource ('newly constituted companies'), and data source ('Spain's BORME'), and distinguishes itself from siblings like borme_lookup or borme_officer_changes by focusing specifically on new company formations rather than general lookups or officer changes.

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 when to use this tool: 'Essential for: competitive intelligence, market monitoring, lead generation, sector analysis, identifying new market entrants.' It also mentions filtering capabilities by sector and province. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different use cases.

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