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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered by structured data. The description adds only 'RETURNS: Egger 检验 P 与截距', which is useful since there is no output schema, but it omits material behavioral context such as the small-study-count reliability caveat and the assumption that effects/SEs are on a comparable scale.

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, front-loaded layout (purpose | USE WHEN | ALIASES | RETURNS) is easy to scan and contains no filler sentences. The alias list is long but earns its place for intent matching; the bilingual duplication of the title is mild redundancy.

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 two-parameter, read-only statistical test with full schema coverage and an explicit RETURNS line standing in for a missing output schema, the definition gives an agent enough to call it correctly. The remaining gap is methodological preconditions (minimum number of studies, effect-size scale) that would prevent misuse.

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% — both log_or and se_log_or carry Chinese descriptions — so the baseline of 3 applies. The description adds nothing about parameter semantics: it never states that the arrays must be parallel, equal-length, or that SE must correspond to the same effect metric as the log OR.

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 names a specific statistical test and its purpose ('Egger's test (publication bias)' plus 'test publication bias in a meta-analysis'), so an agent can immediately tell it is a bias-detection tool rather than a general meta-analysis or plotting tool like forest_plot. It does not explicitly contrast itself with any sibling, so it stops short of a 5.

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: test publication bias in a meta-analysis and get funnel-plot inputs' gives a clear triggering context, and the alias list ('publication bias', 'funnel plot', 'meta回归') helps intent matching. There is no when-not guidance, no minimum-study caveat, and no named alternative (e.g., trim-and-fill or funnel_plot) for when Egger's test is inappropriate.

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