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

With no annotations, the description carries the behavioral burden. It states that the data is 'parsed from the registrar allotment dataset', which clarifies it is a derived analysis rather than raw results. It enumerates the computed dimensions, but it does not disclose data freshness, caching, or any limitations. This is adequate but not rich.

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 a single, efficient sentence that front-loads the tool's purpose and then enumerates the output fields. The bilingual name repetition is minor waste, and the overall structure is easy to scan.

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?

There is no output schema, but the description compensates by listing the expected return content in detail. However, parameter semantics and usage context are missing, so the agent still lacks some information needed for fully confident invocation.

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?

Schema description coverage is 0%, and the description does not explain either parameter. stock_code is self-explanatory in context, but locale with its zh-CN default is not described anywhere. The description fails to compensate for the missing schema 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 identifies the tool as producing an 'allottee profile' with specific demographic dimensions (total lots, person/company split, region/nationality, province, gender, age histogram/pyramid, HK ID letter/era/district). This specific resource and output set distinguishes it from siblings like get_allotment_result or get_allotment_tiers, which cover different aspects of allotment data.

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?

Usage is implied rather than explicit: the agent can infer to use this tool when it needs winner demographics for a stock's allotment. However, there is no direct comparison to sibling alternatives like get_registrar_analysis or get_registrar_research, and no when-to-use versus when-not-to-use guidance.

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