monte_carlo_uncertainty_analysis

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

Despite no annotations, the description discloses key behaviors: sampling from specified distributions, running n_samples simulations, statistical analysis, and error handling ('errors are returned as formatted strings'). It also mentions Latin Hypercube Sampling for efficiency.

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 well-structured with a clear first sentence, then a parameter list, return description, and additional notes. It is concise for a complex tool, though the parameter list uses a docstring style that could be more compact.

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 tool's complexity and lack of annotations, the description covers purpose, parameters, return value (including statistical summaries), and sampling method. Output schema exists but its content is not shown; the description adequately summarizes expected output.

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 0%, so the description must compensate. It provides brief but clear parameter descriptions (e.g., 'uncertainty_parameters: Parameters with uncertainty distributions'), adding meaning beyond the schema's empty property definitions.

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 the tool's purpose: 'Perform Monte Carlo uncertainty analysis on spacecraft trajectory.' This is a specific verb ('perform') and resource ('Monte Carlo uncertainty analysis on spacecraft trajectory'), distinguishing it from siblings like 'trajectory_sensitivity_analysis'.

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 (e.g., sensitivity analysis, Kalman filtering). The description implies use for uncertainty propagation but does not provide when-not-to-use or list of alternative tools.

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