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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered structurally. The description adds only 'RETURNS: 推荐统计方法与代码', which usefully signals that the tool also recommends a statistical method, not just code. It says nothing about whether the generated code is executed, validated, or version-pinned, which matters for a 'reproducible code' claim.

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 pipe-delimited USE WHEN / ALIASES / RETURNS layout is front-loaded and scannable, with the purpose stated first. It is somewhat keyword-stuffed (aliases in two languages, bilingual title repetition) but no sentence is truly wasted.

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?

With no output schema, the one-line RETURNS note is the only return-value signal and is vague — it does not say whether output is one script or three language variants. For a 3-parameter generation tool with full annotation coverage this is adequate but leaves real gaps around output shape and language selection.

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 'design', 'n_groups' and 'outcome_type' are already documented in the schema. The description adds no meaning beyond that — it never explains how design/outcome_type steer method or language selection. Baseline 3 applies when the schema carries the parameter burden.

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 gives a specific verb and resource: 'generate the SAS / R / Python code that reproduces a given analysis', which is far clearer than the bare name 'sas_r_code' (which omits Python entirely). It does not, however, contrast itself with plausible siblings such as stats_toolbox or dataframe_parse, so an agent must infer the boundary.

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?

'USE WHEN: generate the SAS / R / Python code that reproduces a given analysis' is an explicit trigger condition, which is stronger than most siblings' implicit usage. It offers no when-not guidance and names no alternative tool for cases where the agent wants analysis results rather than code.

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