ENTIA Entity Verification

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

Annotations already indicate this is a read-only, non-destructive, idempotent operation with a closed world. The description adds valuable context beyond annotations: it specifies the data sources (INE, SEPE), coverage scope (8,131 municipalities), and the types of data returned (population, business count by sector, unemployment). This enhances understanding of what the tool provides 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 well-structured and front-loaded with the core purpose. Each sentence adds value: the first states the purpose, the bullet points detail data categories, and the final sentence provides scope and source information. There is no wasted text, making it efficient and easy to parse.

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 (retrieving multi-source demographic data), the description is complete. It explains what data is returned, the coverage scope, and data sources. With annotations covering safety and behavioral traits, and an output schema presumably detailing the response structure, the description provides all necessary context 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?

The input schema has 100% description coverage, with the 'municipality' parameter clearly documented. The description does not add any additional parameter semantics beyond what the schema provides, such as format examples or constraints. However, with high schema coverage, a baseline score of 3 is appropriate as the schema already 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 the complete demographic and business profile for any Spanish municipality.' It specifies the exact data retrieved (population, business count, unemployment) and distinguishes itself from siblings like 'zone_profile' by focusing on municipality-level data rather than broader zones.

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: for obtaining demographic and business profiles of Spanish municipalities. It implicitly distinguishes from siblings like 'entity_lookup' or 'search_entities' by focusing on municipalities rather than businesses or professionals. However, it does not explicitly state when NOT to use it or name specific alternatives.

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