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

The description discloses the read-only nature consistent with annotations and explains the data lag behavior, including that queries newer than the latest published week return zero rows with a data_lag_note. It also clarifies that this represents lag rather than absence of new companies, which is valuable for correct interpretation.

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 tool's purpose, output, and usage examples. It is slightly repetitive around the 'since' date and the lag note, but the repetition reinforces important operational details without significant bloat.

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?

For a simple list/query tool with three optional parameters and no output schema, the description provides all necessary context: what data is returned, the date semantics, the lag behavior, and example usage. The lack of an explicit output schema is mitigated by the description naming the returned fields.

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 schema already provides clear descriptions for all three parameters (type, limit, since), achieving 100% coverage. The description does not add additional parameter semantics beyond restating the core 'since' concept, so it stays at the baseline for high schema coverage.

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 identifies the resource (Companies Registry newly incorporated/registered companies), the scope (Hong Kong local and non-Hong Kong), and the data source (official weekly open-data feed via data.gov.hk). It also distinguishes this tool from static company lookup by emphasizing 'new' companies and provides example use cases.

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 explicitly states when to use the tool ('Use for "new Hong Kong companies this week"...') and includes important operational caveats about the publication lag and the resulting zero-row response with a data_lag_note. This gives an agent clear guidance on invocation and interpretation.

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