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. The description adds one genuinely useful behavioral claim — that a real algorithm engine performs the fit rather than the LLM — but says nothing about default model selection, minimum data points, convergence failure behavior, or how the confidence interval is derived.

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 compact clauses: purpose/inputs first, then outputs and the execution-engine note. No filler, though the bracketed title prefix slightly duplicates the tool title in annotations.

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 description correctly supplies the return values (IC50 and 95% CI), which is valuable. However, for a 4-parameter fitting tool it omits practical essentials: defaults for model and direction, required minimum number of concentration points, unit/format expectations for the string arrays, and what happens on a failed fit.

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 the baseline of 3 applies. The description adds no meaning beyond the schema for 'model' (4PL/3PL), 'direction' (inhibition/activation), or the array-of-string format of concentrations and responses; notably it does not say which model is used when 'model' is omitted.

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 ('IC50 曲线拟合') plus the required input pairing (drug concentration + inhibition rate) and the output (IC50 with 95% CI). This clearly separates it from sibling analysis tools such as act_dose_convert_pharm or act_pkpd_eval, though no sibling is named explicitly.

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?

Usage is implied by the phrase '用户有药物浓度和抑制率要算 IC50', which tells an agent the prerequisite data shape. It does not state when to prefer this over adjacent tools (e.g., dose conversion or PK/PD evaluation) or what to do when prerequisites are not met.

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