predict_expected_purchases

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

No annotations are provided, so the description carries the full burden. It clarifies that it works on a fitted BG/NBD model, but does not disclose whether it mutates state, requires specific permissions, what the output format is, or whether predictions are per period or cumulative. The phrase 'from a fitted purchase model' hints at a read-only operation but does not confirm it.

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 clear sentence with no fluff. It front-loads the core purpose. Could include a usage hint, but for its length it's well structured.

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 1 parameter with 0% schema coverage, no output schema, and no annotations, the description is insufficient. It does not explain how 'top_n' and 'future_t' interact, the return format, or any edge cases (e.g., what if model is not fitted). An agent would need more info to call it correctly.

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%, so the description must compensate. The description adds that the model is BG/NBD and points to 'purchase model', but it does not explain the semantics of 'top_n' (e.g., cutoff threshold) or 'future_t' (period units: days, weeks?). The schema provides basic descriptions like 'Return top N customers' and 'Number of future periods', but the description adds little beyond that.

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 verb ('predict'), a resource ('expected future purchase counts per customer'), and mentions the model type (BG/NBD). It distinguishes from sibling tools like 'predict_expected_spend' and 'predict_customer_clv' which predict different quantitiesable. However, it doesn't explicitly contrast with 'predict_expected_spend' or 'predict_probability_alive', which might lead to confusion.

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 usage for post-fitting prediction, but does not state when to use this tool over siblings such as 'predict_expected_spend' or 'predict_probability_alive'. No exclusions or alternatives are mentioned, so the agent must infer the scope from the tool name.

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