monte_carlo_simulation

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

The description explains the output (distribution, percentiles, loss probability) but does not disclose whether the tool is read-only, modifies any data, or has authentication requirements. No annotations are provided, so the description carries the full burden; it partially meets it.

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 a single, clear sentence that is easy to parse. It is brief but covers the core functionality. Slightly more structure (e.g., bullet points for key features) could improve usability, but it is already concise.

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?

Given the complexity of Monte Carlo simulation and the absence of an output schema, the description provides a high-level overview but lacks details on return format, interpretation, or limitations (e.g., free tier restrictions are in the schema but not in the description). It is adequate but not fully complete.

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 all parameters have descriptions in the schema. The description itself does not add new meaning beyond the schema. A baseline score of 3 is appropriate.

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 clearly states it runs a Monte Carlo simulation on a portfolio to model the distribution of future returns, including percentile outcomes and probability of loss. This distinguishes it from sibling tools like stress_test (scenario-based) or analyze_risk (general risk assessment).

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?

No explicit guidance on when to use this tool versus alternatives such as stress_test or analyze_risk. There is no mention of prerequisites or context where this tool is preferable.

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