predict_expected_spend

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

There are no annotations, so the description carries the burden. The verb 'Predict' implies a read-only inference, and 'from a fitted Gamma-Gamma value model' states the prerequisite. However, it does not disclose behavior such as error handling, side effects, or what happens if the model is not available.

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. Every word contributes to identifying what the tool does and what it depends on.

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 is adequate for a simple prediction tool: it states the output and the prerequisite model type. However, with no annotations and no output schema, it does not explain the optional top_n parameter or the expected return shape, leaving some inference burden on the agent.

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 Gamma-Gamma model context and the spend metric, which indirectly relates to model_id, but it does not mention top_n or parameter names. Given the context signal of 0% schema description coverage, the description should compensate for parameter details but fails to do so.

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 specific resource ('average transaction monetary spend per customer'), and the required model type ('fitted Gamma-Gamma value model'). This clearly distinguishes it from siblings like 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 phrase 'from a fitted Gamma-Gamma value model' clearly implies this tool should be used after fitting a value model and not before. It provides clear context, though it does not explicitly name alternatives or when not to use it.

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