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

With no annotations, the description carries the full behavioral burden. It discloses data provenance (official announcement archive), integrity (SHA-256 fingerprint), sample size, and the non-predictive nature. However, it omits operational details such as read-only status, authentication needs, rate limits, and error behavior, leaving clear gaps.

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?

Two tightly written sentences with zero waste, front-loading the core purpose and data provenance. The disambiguation is placed after the main clause, maintaining clarity and flow.

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 no output schema, the description mentions return content (SHA-256 fingerprint and sample size) and the data source. For a simple historical data query, this is largely complete, though the response structure and pagination behavior are not detailed.

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 100%, so all three parameters (issue, limit, stats) are fully documented in the schema. The description adds no additional parameter-level meaning or syntax beyond what the schema already provides. Baseline 3 is correct when the schema 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 states a specific verb and resource: '结构化查询双色球历史开奖数据' (structured query of Double Color Ball historical draw data). It adds data provenance and a clear disambiguation ('数据检索，不是预测'), distinguishing it from predictive tools. However, it does not explicitly name sibling alternatives, so a 4 is appropriate.

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 negative guidance by stating it is data retrieval, not prediction, and that no number selection advice is offered. This implies when not to use it, but it does not state positive when-to-use conditions or alternatives. Minimum viable guidance for a query tool.

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