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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuine value by disclosing that a real algorithm engine performs the computation rather than an LLM, which speaks to result trustworthiness. It does not cover validation behavior (e.g. equal-length arrays, minimum study count) or whether a funnel plot is actually returned.

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?

Compact and front-loaded: bracketed title, then use case, then return values. Nothing is padded or redundant. Slight compression makes the funnel-plot clause ambiguous, which is the only structural weakness.

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 statistics tool with no output schema, the description reasonably covers purpose, when, and what comes back. It omits prerequisites an agent would need to call it correctly, notably the minimum number of studies required for a valid Egger test and the expectation that both arrays be the same length and pre-transformed to log scale.

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 coverage is 100% — both required arrays (logOR and SE per study) are documented in the schema. The description adds no syntax or format detail beyond that, so the baseline 3 applies.

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?

Names a specific statistical procedure: Egger's test for publication bias in meta-analysis, and states the outputs (Egger P value and intercept). That is a clear verb+resource, and no sibling in the list covers publication bias, so confusion risk is low. The mention of '画漏斗图' (draw funnel plot) slightly muddies scope, since the stated return values are only P and intercept.

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 phrase '用户做 Meta 分析要检验发表偏倚' gives the implied context of use — during a meta-analysis when publication bias must be tested. There is no explicit when-not guidance, no minimum-study-count prerequisite, and no pointer to related tools such as act_forest_chart for visualization.

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