coldbrew_recommend

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

With no annotations, the description carries the behavioral burden and does so well: it discloses auto-exclusion of matcha/cocoa/tea paste/medicinal tea pills, the default-current-time behavior for datetime_str, and the meaning of randomness and count. It only omits edge-case behaviors such as empty inventory, which is minor for a recommendation tool.

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 front-loaded with a short purpose phrase, then gives outputs, exclusions, and parameter meanings in a tight structured list. No sentence is wasted or redundant with the schema.

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?

For a tool with no required parameters and an output schema, the description covers the essential invocation context: defaults, ranges, output fields, and excluded inputs. Missing behavior like empty-inventory errors is not necessary for correct 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?

Schema description coverage is 0%, but the description fully compensates by explaining datetime_str's ISO format/default, randomness's 0-1 range/default, and count's max/default. This is exactly the semantic information an agent needs beyond the bare schema fields.

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?

Description states a specific action: recommend cold-brew tea for a 1.6L cold-brew pot at refrigerator temperature, and names concrete outputs (tea amount, cold-brew duration, ready-to-drink time). This clearly separates it from the generic sibling `recommend` by scoping it to cold brewing.

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 intended scenario is explicit: use when the user wants a cold-brew tea recommendation for the 1.6L pot, with automatic exclusion of unsuitable tea forms. It does not explicitly name sibling alternatives or state when to use `recommend` instead, so it stops short of full alternative routing.

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