how_much_product_was_used

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

The description explains important behavioral details beyond the annotations: days are ignored if start_date is set, end_date defaults to today, and material_name works as a filter. This adds context on parameter precedence and time range handling. The readOnlyHint and idempotentHint align with the description, and no contradictions are present.

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 well-structured: it opens with example questions, gives a one-sentence summary, and then provides a concise parameter list. Every sentence adds value, and the format is easily scannable. It is neither too terse nor overly verbose.

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 covers the tool's purpose, parameter semantics, and key behavioral nuances (e.g., date range handling, filtering). An output schema exists, so not explaining return values is acceptable. It could mention what happens when no data matches, but the current level is sufficient for effective selection and invocation.

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 input schema provides zero property descriptions, but the description's Args section thoroughly explains each parameter—including defaults, format (YYYY-MM-DD), precedence rules, and an example material name. This fully compensates for the schema's lack of descriptions, giving the agent clear guidance on how to construct valid calls.

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 states the tool's function: 'Totals logged material/chemical usage from completed jobs.' It also provides example questions that make the purpose immediately understandable. This distinguishes it from sibling tools like what_products_do_we_carry or list_materials, which focus on inventory rather than usage.

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 example questions ('How much product was used?', 'What chemicals did we use this month?') clearly indicate when to use the tool. The description also specifies the scope (completed jobs) and optional filters, providing clear context for when it applies. It does not explicitly mention alternatives or exclusions, but the usage context is well implied.

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