fit_clv_model

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

No annotations are provided, so the description must carry the behavioral disclosure burden. It discloses deprecation status and the model family, but not what happens during fitting, whether artifacts are written, how long it runs, or what the return value is.

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?

A single front-loaded sentence plus a short parenthetical deprecation note. Every token earns its place, and the most important behavioral signal (deprecation) is clearly set off.

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?

For a tool with a complex nested config requiring column mappings, sampler settings, and model types, one sentence is incomplete. There is no output schema and no annotation coverage to offset the missing details about required config, return values, or side effects.

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% and the description does not mention any parameters or config fields. The nested schema has its own property descriptions, but the tool description itself adds no parameter meaning to compensate for the low coverage.

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 ('Fit'), a specific resource ('Bayesian Customer Lifetime Value model'), and a specific input domain ('RFM transaction data'). It also names sibling alternatives, which differentiates it from the many other 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?

Explicitly warns the tool is deprecated and instructs the agent to prefer fit_purchase_model or fit_value_model. This is direct when-not-to-use guidance, even if it does not detail when to choose between those two alternatives.

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