Get top customers by total spend

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It transparently covers exclusions ('cancelled' orders), tie-breaking (customer id ascending), interval semantics (half-open UTC, YYYY-MM-DD, open bounds), and rejection conditions (from must be earlier than to). This level of detail goes well beyond typical descriptions and leaves little ambiguity.

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 dense but every sentence adds essential information. It front-loads the core ranking logic, then systematically covers edge cases (exclusions, tie-breaking, defaults, intervals) and ends with a concrete usage example. There is no fluff or repetition; the structure logical and efficient.

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 comprehensively explains behavior and parameters, but it does not specify the return format (e.g., what fields are returned for each customer). Since there is no output schema, the agent may not know if the result includes customer IDs, names, or just the spend amount. It also does not explicitly state that the interval applies to order dates, though this is reasonably inferred. These minor gaps prevent a perfect score.

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 fully compensate. It explains 'limit' with default and usage (single top spender vs top-N), and thoroughly documents 'from' and 'to' including format, inclusivity, open-bound behavior, and ordering constraints. Every parameter is meaningfully described, adding significant value beyond the raw schema.

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 states a specific verb ('Ranks customers by total money spent'), defines the metric precisely (sum of quantity * unit_price), and clearly differentiates from the sibling get_top_customers_by_orders by focusing on spend rather than order count. It also provides a concrete usage phrase ('who is the customer who spent the most money?'), making the tool's purpose unambiguous.

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 gives a clear when-to-use via the example question, and explains parameter adjustments and interval semantics, but it does not explicitly contrast with sibling tools like get_top_customers_by_orders or state when NOT to use this tool. The metric distinction is implied, but not spelled out, so the agent has to infer the appropriate alternative.

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