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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description goes well beyond them: it discloses the normal-distribution assumption, warns that this understates tail risk, and frames the output as a property of inputs rather than a prediction. Those caveats are exactly the context an agent needs to avoid over-claiming results.

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?

Three tight paragraphs in a deliberate order: what it does and returns, when to use it versus alternatives, then modeling caveats. Every sentence carries routing or behavioral information; nothing is 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?

There is no output schema, yet the description enumerates the return contents (percentiles, mean, median, goal-hit share), the distributional assumption, and the tool's limits. For a 9-parameter simulation tool, an agent has everything needed to call it and to report results honestly.

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; the description adds cross-parameter meaning by stating that returns are drawn from a normal distribution using the supplied expected return and volatility, and that goal_amount conditionally produces a hit-rate. It doesn't add detail on simulations, fee drag, or inflation beyond what the schema already documents.

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 ('Runs a Monte Carlo simulation over a portfolio balance') and immediately names the output shape: percentiles, mean, median, and goal-hit share. It explicitly distinguishes itself from calculate_dcf_value and get_stock_valuation by naming them as the wrong tool for valuation questions.

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?

Gives an explicit trigger ('when the user asks about the range or probability of outcomes rather than a single projected number') with concrete examples (odds of reaching a target, volatility widening the spread). It also lists explicit exclusions — company valuation with named alternatives, plus retirement withdrawal, tax, and drawdown sequencing — which the tool does not model.

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