estimate_customer_lifetime_value

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

With no annotations, the description carries the burden of behavioral disclosure. It does reveal that the tool computes a discounted net present value and requires fitted models, but it does not state side effects (e.g. read-only vs model mutation), compatibility expectations between the two model IDs, or the shape of the returned estimate.

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 focused sentence that leads with the action and resource, then adds method details and concrete example models. Every clause earns its place with no filler.

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 read-only estimation task and the schema covers parameter semantics. However, with no output schema and no annotations, it omits information about return format, error behavior if models are not fitted or incompatible, and whether this should be used instead of close siblings like predict_customer_clv.

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 tool description does not explain any parameters, but the input schema fully documents each nested field (top_n, future_t, discount_rate, value_model_id, purchase_model_id) with descriptions and defaults. Since the schema does the heavy lifting, the missing parameter detail in the description is acceptable.

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 uses a specific verb ('Estimate') and a concrete resource ('discounted net present Customer Lifetime Value (CLV)'), and it identifies the key inputs (fitted purchase and monetary value models). However, it does not explicitly differentiate this from the sibling tool predict_customer_clv, which likely performs a similar CLV prediction.

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 when to use the tool: after fitting a purchase model (e.g. BG/NBD) and a monetary value model (e.g. Gamma-Gamma), because it combines those fitted models. It does not state when not to use it or which sibling to prefer instead, such as compare_models or predict_customer_clv.

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