ENTIA Entity Verification

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

The description adds valuable context beyond annotations by specifying that metrics are 'real-time' and sourced from 'BigQuery about the verified entity registry,' which informs data freshness and origin. Annotations already cover safety (readOnlyHint=true, destructiveHint=false, idempotentHint=true), so the description appropriately supplements without contradiction.

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 and efficient, using two sentences that directly state the tool's function and data source without unnecessary details. Every sentence contributes essential information, making it highly concise and well-structured.

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 simplicity (0 parameters, annotations covering safety, and an output schema present), the description is complete. It explains what statistics are retrieved and their source, which, combined with structured fields, provides sufficient context for an AI agent to use the tool effectively.

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 0 parameters and 100% schema description coverage, the baseline is high. The description clarifies that no inputs are needed and the tool fetches platform-wide statistics, adding semantic meaning about the scope of data returned, which compensates for the lack of parameters.

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 with specific verbs ('Get') and resources ('ENTIA platform-wide statistics'), listing concrete metrics (total entities, countries, sectors, coverage). It distinguishes itself from sibling tools by focusing on platform-wide statistics rather than entity-specific lookups or verifications.

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 implies usage context by specifying 'platform-wide statistics' and 'real-time metrics from BigQuery about the verified entity registry,' suggesting it's for high-level analytics. However, it does not explicitly state when to use this tool versus alternatives like 'entity_lookup' or 'search_entities,' which might provide more detailed data.

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