get_customer_ltv

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

With no annotations, the description discloses the return format ('JSON string with LTV, ARPU, average lifespan, and top/bottom 5 merchants') and the optional app_id filter. However, it does not mention any behavioral constraints such as data requirements, error handling, or whether the metrics are computed in real-time. This is minimal coverage.

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 compact and well-structured with an Args/Returns format. Each line serves a purpose with no waste.

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 tool is simple: one optional parameter and a well-defined return set. The description covers the purpose, parameter, and return structure. With an output schema present, this is sufficient, though it could add a brief usage note.

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 schema only provides the parameter name 'app_id' with a default value; the description adds that it is an optional filter by app. This gives enough meaning for a single parameter, compensating for the 0% schema 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 clearly identifies the tool's function: computing customer lifetime value, ARPU, and merchant rankings. However, it does not explicitly differentiate from sibling analytics tools like get_revenue_anomalies or get_merchant_health, so it earns a 4 rather than a 5.

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 provides no guidance on when to use this tool versus alternative metrics tools. It only explains the optional app_id parameter, which is parameter semantics, not usage context. This is a clear gap.

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