predict_probability_alive

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

No annotations are provided, so the description carries the full behavioral burden. It only states an estimation action; it does not disclose whether the operation is read-only, what the response shape is, or whether any model state or requirements matter beyond having a fitted model.

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, front-loaded sentence with no filler. It communicates the core action and object efficiently.

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?

With no annotations and no output schema, the description should clarify what the tool returns and how top_n changes the result. It does not explain the return shape or whether it returns one probability, a list of customers, or a scored table, leaving an agent to infer important behavior.

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 mentions the fitted model, which maps to model_id, but it does not mention the optional top_n parameter. The input schema already describes both parameters clearly, so the description reaches the schema-covered baseline without adding much meaning beyond it.

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 names a specific action ('Estimate'), a specific output ('probability of customer retention/alive'), and a source ('fitted purchase or churn model'). This is clear enough to distinguish it from sibling spend/purchase prediction tools, though it does not explicitly name an alternative.

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?

The phrase 'from a fitted purchase or churn model' implies the tool is used after model fitting and for retention/alive probability questions Echo. However, there is no explicit when-not-to-use guidance or comparison to alternatives such as predict_expected_purchases or get_churn_risk_cohorts.

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