monte_carlo_schedule

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

Annotations indicate readOnly, non-destructive, idempotent. Description adds that it samples from triangular distributions and returns percentiles with risk events, providing useful behavioral detail beyond 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?

Two sentences: first states purpose, second details methodology and key parameters. Every word adds value, no redundancy.

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?

No output schema, but description specifies return values (P10/P50/P80/P95 estimates, risk events). Could clarify what constitutes risk events, but overall sufficient given input schema richness.

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 covers 100% of parameters with descriptions. Description reinforces seed usage and explains that durations are sampled from triangular distributions, adding context that the three-point estimates are used to generate output percentiles.

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?

Description clearly states the tool runs Monte Carlo simulation for probabilistic schedule risk analysis, specifies inputs (triangular distributions) and outputs (P10/P50/P80/P95 estimates with risk events), and distinguishes from sibling tools like pert_estimate and critical_path.

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?

Description mentions using seed for reproducibility but gives no explicit guidance on when to choose Monte Carlo over alternatives like pert_estimate or schedule_risk. Usage context is implied but not formally stated.

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