power_curve

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

Beyond the readOnlyHint annotation, it discloses sampling behavior, automatic column-name recognition with fallback to explicit columns, and the concrete output composition including first/median/last power, growth multiplier, and shape classification. This is substantial behavioral context that the annotation alone does not provide.

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 description is dense but every clause earns its place: input, sampling, output, auto-mapping, fallback behavior, and intended use case. It is front-loaded with the core batch-curve purpose and contains no filler.

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 still enumerates the output fields and shape classifications. The schema covers all parameters and the annotation marks the tool read-only, so nothing essential is missing for correct invocation and interpretation.

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%, so the baseline is 3, but the description adds real parameter semantics: concrete auto-name mappings (攻击力→attack, 体力→hp, etc.), explicit column-mapping examples, and the guarantee that first/last rows are always included when sampling. This raises it above baseline.

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 uses a specific action plus resource: it reads a growth table and produces a level-to-power curve with shape summary metrics. It explicitly distinguishes itself from compute_power by presenting itself as the batch/full-curve version and stating it replaces repeated single-point calls.

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?

It clearly states when to use the tool: full-curve analysis and growth × equipment joint derivation, and it names compute_power as the single-point alternative it replaces. It does not explicitly state a when-not-to-use condition, but the context is clear enough for an agent to choose correctly.

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