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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered by structured data rather than prose. The description adds one genuinely useful behavioral fact beyond that: the return is a vector graphic (RETURNS: 森林图（矢量）). It says nothing about validation of the input array, minimum number of studies, or failure behavior for malformed rows — but with annotations present the bar is lower.

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?

It is short and front-loaded, but a large share of the text is a bilingual alias list (森林图, 置信区间图, meta森林图) that is keyword filler rather than actionable guidance. The pipe-delimited format is scannable and nothing is buried, so it is efficient but not dense with value.

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 3-parameter, single-purpose plotting tool with no output schema, the definition covers purpose, trigger, and return type, which is enough to make a reasonable call. What is missing is anything about data expectations (minimum rows, required numeric fields) or how the returned vector figure is delivered for use.

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% and each parameter, including the required 'data' array and its per-item {name, estimate, ci_lower, ci_upper} shape, is documented in the schema itself. The description contributes no additional parameter meaning such as value ranges, scale (log vs linear), or whether estimate is expected on a log scale. Baseline 3 is correct.

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 the concrete artifact (forest plot) and the required input form (HR/OR/RR with confidence intervals), which is specific enough to separate it from siblings like kaplan_meier, egger_bias_test, or nomogram. It never states an explicit verb (e.g. 'generate/render'), so the action is only implied by the title and the RETURNS line.

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 'USE WHEN' clause gives a real trigger condition: use when you have HR/OR/RR effect estimates with CIs. There are no exclusions and no named alternatives, so an agent must infer when a different plotting tool (e.g. kaplan_meier, roc_auc) is the better fit.

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