ENTIA Entity Verification

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

The description adds valuable behavioral context beyond what annotations provide. While annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, the description elaborates on the data sources (AEAT, SEPE, INE, ENTIA), coverage (11,241 postal codes), and the specific computed metrics. This helps the agent understand the richness and reliability of the data being returned.

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 perfectly structured and concise. It starts with the core purpose, specifies coverage, then organizes the return data by source with clear bullet points. Every sentence and bullet point adds essential information about what data is returned, with zero wasted words or redundant information.

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 (returning comprehensive socioeconomic data from multiple sources), the description provides excellent completeness. It details all data sources and specific metrics returned, which complements the existing annotations and output schema. The agent has a complete picture of what this tool does and what data to expect without needing to rely solely on the output schema.

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 fully documents the single 'postal_code' parameter. The description doesn't add any additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate since the schema does all the heavy lifting for parameter documentation.

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 the full socioeconomic profile for any Spanish postal code.' It specifies the exact resource (socioeconomic profile), geographic scope (Spanish postal codes, 11,241 covered), and distinguishes itself from siblings like 'municipality_profile' by focusing on postal code granularity rather than municipal level.

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: when socioeconomic data at the postal code level is needed. It implicitly distinguishes from 'municipality_profile' by specifying postal code granularity. However, it doesn't explicitly state when NOT to use it or name specific alternatives, though the sibling list suggests potential alternatives like 'municipality_profile' for broader geographic data.

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