Sensitivity analysis (variance drivers)

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

With readOnlyHint, openWorldHint=false, and idempotentHint already declared, the description adds real behavioral context: it performs a Monte Carlo simulation, computes correlation and variance share per task, and ranks results. It does not mention the reproducible-by-default seed behavior (left to the schema) or the compute cost of large iteration counts, but it goes meaningfully beyond the annotations.

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?

Purpose is front-loaded in the first clause, followed by mechanism, output shape, and a memorable framing metaphor. Four tight sentences with no filler, each earning its place.

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 the description covers purpose, mechanism, and output ranking adequately. It could note prerequisites (minimum two tasks, distribution choice) that live only in the schema, but nothing essential for correct invocation is missing.

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 80% and the schema already documents seed, unit, iterations, distribution, and the nested task fields in detail. The description adds no parameter-level syntax or format guidance beyond what the schema provides, so the baseline 3 applies.

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+resource (identify which tasks drive uncertainty in a project total) and explains the mechanism (runs Monte Carlo, computes per-task correlation and variance share). It returns ranked drivers and even frames the output ('the data behind a tornado chart'), which clearly separates it from the forecast_* and pert_estimate siblings.

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 conveys the value proposition ('so you know where reducing estimate uncertainty or de-risking the work has the most impact'), which implies when to reach for it. However, it never names an alternative or states when NOT to use it versus forecast_duration, forecast_cost, or pert_estimate, leaving the routing decision to inference.

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