bootstrap_decline

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

Given no annotations, the description covers the core behavior: resampling with replacement, refitting, and returning confidence intervals. However, it does not detail whether the function is read-only or has side effects, and the output format is only vaguely described.

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 concise, structured with an Args section, and contains no unnecessary words. Every sentence adds value.

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?

The description sufficiently covers inputs, process, and output for a bootstrap tool. Since an output schema exists, it is acceptable that return values are not fully detailed. However, it could mention typical assumptions or limitations.

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 0%, so the description compensates by explaining each parameter: production_data as list of dicts with 'time' and 'rate', model options, and num_bootstrap with default. This adds substantial meaning beyond the schema, though the exact dict keys could be more precise.

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 bootstraps decline curve parameters from production data, explaining the resampling process and output of confidence intervals. It is specific and distinct from sibling tools like fit_decline or decline_sensitivity.

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. It lacks comparisons to other decline curve tools or conditions for use, leaving the agent to infer usage.

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