bootstrap_decline

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

The description discloses key behavioral traits: resampling with replacement, refitting the model, and returning confidence intervals. Since no annotations are present, the description carries the full burden and does so adequately, though it omits details like output format or assumptions about data distribution.

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, using a short docstring format. The first sentence states the purpose, followed by a clear process explanation and parameter list. Minor redundancy exists (e.g., 'Bootstrap' repeated), but overall it is efficient and front-loaded.

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 (bootstrap, uncertainty quantification) and presence of an output schema, the description covers the main flow. However, it lacks assumptions (e.g., data sufficiency, model fit criteria) and does not guide against sibling tools. It is minimally complete for a competent agent.

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?

The description adds significant meaning beyond the input schema, which lacks descriptions (0% coverage). It explicitly explains each parameter: production_data format (dicts with 'time' and 'rate' keys), model options ('exponential', 'hyperbolic', 'harmonic'), and num_bootstrap default (1000). This fully compensates for the schema's lack of detail.

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 bootstraps decline curve parameters from production data, a specific verb and resource. It explains the process but does not explicitly distinguish from sibling tools like fit_decline or eur_distribution, which perform related but distinct tasks.

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 guidance is provided on when to use this tool versus alternatives. The description lacks context on prerequisites, use cases, or exclusions, leaving the agent to infer usage from the tool name alone.

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