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

Annotations already establish that this is a read-only, non-destructive, closed-world computation, so the safety profile is covered. The description's main added value is stating the return payload (IC50 with 95% CI), but it says nothing about how the model choice affects results, default model behavior, or what happens with insufficient/degenerate data points.

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 layout front-loads the purpose, then trigger, aliases, and return value in a compact block with no filler sentences. It is efficient, though the alias dump is longer than strictly needed for tool selection.

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 4-parameter computation tool with a fully documented schema and no output schema, the description correctly states the return value, which fills the main gap. It still omits practical requirements such as minimum number of concentration points, model defaulting, and how direction is inferred, which matter for correct invocation.

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%, with every parameter (model, direction, responses, concentrations) documented in Chinese, so the baseline is 3. The description adds no syntax, default values, or constraints beyond the schema, and notably does not clarify what 'direction: inhibition vs activation' does to the fitted result.

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 verb+resource combination ('fit a dose-response curve and report IC50 with 95% CI'), which tells an agent exactly what the tool produces. It stops short of distinguishing itself from nearby siblings such as calibration_curve or cell_assay_calc, so it is clear but not fully differentiated.

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 explicit 'USE WHEN: fit a dose-response curve and report IC50 with 95% CI' gives a concrete trigger condition, and the ALIASES list clarifies the vocabulary users may bring (IC50, GI50, dose response, 抑制率拟合). No when-not-to-use or named alternative is offered, so it falls short of the top band.

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