get_churn_risk_cohorts

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

With no annotations provided, the description carries the behavioral burden. It clearly explains the filtering rule, default threshold, and the confidence interpretation, but it does not disclose output format, error behavior, or whether the operation has side effects. It is accurate but not deeply transparent.

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?

Three sentences with no filler. The purpose is front-loaded, followed by the core behavioral rule and default value, and every sentence adds useful information.

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 tool is simple and the description covers selection and threshold behavior, but there is no output schema and the description does not explain the shape of the returned customers or whether additional fields like P(alive) are included. This leaves some ambiguity for an agent invoking the tool.

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 must compensate. It explains threshold_p_alive well, including the default value and the inverse confidence relationship, and it ties model_id to 'a fitted CLV model.' It does not state the valid numeric range for the threshold, but the probability context makes it reasonably inferable.

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 states a specific verb and resource: identify churn-risk customers from a fitted CLV model. It further distinguishes itself by specifying that it returns customers whose Bayesian P(alive) is below a threshold, which separates it from sibling prediction and model-fitting tools.

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 description implies it should be used after fitting a CLV model and explains how to interpret the threshold, but it does not explicitly compare against alternatives like predict_probability_alive or state when this tool should be preferred. No exclusions are given, only implied context.

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