pm.pert

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

Beyond the readOnly/idempotent annotations, it discloses a concrete failure condition (optimistic > pessimistic is an error), a precision rule (no rounding, stddev to 30 significant digits), and a unit-consistency requirement across the three inputs. These are exactly the behavioral traits annotations cannot express.

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?

Four tight sentences, front-loaded with purpose and formulas, then constraints, then the sibling routing hint. Every sentence carries information an agent needs; there is 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?

An output schema exists, so return values need not be explained, and everything else an agent needs — semantics, units, error condition, precision, and alternative tools — is present. Complete for a deterministic three-input calculator.

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 0%, so the description must carry parameter meaning, and it does: it identifies the three inputs as optimistic/most-likely/pessimistic, requires them to be Decimal strings in the same time unit, and imposes the O ≤ P ordering constraint. It stops just short of mapping each parameter name individually, but the formulas make the roles unambiguous.

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?

States a specific verb and resource ('단일 작업의 PERT 삼점 추정으로 기대 기간, 분산, 표준편차를 계산한다') and includes the exact formulas, so an agent knows precisely what is computed. It also explicitly distinguishes itself from siblings by naming critical_path and monte_carlo_schedule for multi-task aggregation.

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?

Explicitly scopes the tool to a single task and routes the agent elsewhere ('여러 작업의 합산 일정에는 critical_path 나 monte_carlo_schedule 을 쓴다'), giving both when-to-use and the named alternatives. Nothing is left to inference.

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