predict_customer_clv

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It only restates the core function and deprecation; it does not mention output shape, side effects, limits, or any assumptions of the BG/NBD prediction.

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 sentence plus a parenthetical deprecation warning. It is front-loaded with the primary action and contains no filler, making it well-structured and appropriately concise.

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 plus the nested schema gives enough context for a simple prediction tool: required model_id, optional future_t and top_n_customers, and clear deprecation routing. The absence of an output schema and any behavioral detail prevents it from being fully complete.

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?

Context signals report 0% schema description coverage, so the description needed to compensate for parameter meaning. It only implies model_id through 'fitted BG/NBD model' and does not clarify future_t or top_n_customers in the description text.

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 identifies a specific verb and resource: generating per-customer CLV predictions from a fitted BG/NBD model. It also names the preferred sibling tools, distinguishing its purpose from predict_expected_purchases and estimate_customer_lifetime_value.

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 deprecation notice explicitly instructs the agent to prefer predict_expected_purchases or estimate_customer_lifetime_value, providing a clear when-not-to-use signal and alternatives. It does not state the exact conditions under which this deprecated tool should still be called, which keeps it from a 5.

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