lookup_work_requirements

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

No annotations are provided, so the description must disclose behavior. It does: it notes that an empty category returns an empty list with matched_count=0 and is a successful result, not an error. It also states the exclusive parameter requirement. It does not go into depth about authorization, reversible side effects, or detailed response structure, but for a read-only lookup this is adequate. The empty-result behavior is a valuable disclosure beyond what a schema would imply.

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 four sentences, front-loaded with the core purpose, then usage guidance, then a constraint, then a behavioral nuance. Every sentence contributes information; there is no fluff. The structure guides the agent from 'what' to 'how' to 'edge case' efficiently.

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?

This is a straightforward lookup tool with two optional parameters and an output schema (not shown). The description covers the trigger conditions, the parameter rule, and the successful empty-result case. Since an output schema exists, defining the return format is not necessary. All information needed to invoke the tool correctly and interpret a common edge case is present.

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 already provides rich descriptions for both parameters (enums with names and descriptions), so the baseline is 3 given high schema coverage. The description adds the critical mutual-exclusivity rule ('never both') which is also in the schema description, but the tool description reinforces it in a clearer, action-oriented way. It also mentions the two parameter names, which helps an agent map the rule to the fields. Slight added value over the schema, hence 4.

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?

States a specific verb and resource: 'Return the normative weather constraints for construction work types.' It also explains the two call patterns (by work_code or category) and clearly distinguishes this tool from siblings like validate_work_window and estimate_curing_time, which address different concerns. The phrase 'Call this first' reinforces its role as a primary lookup.

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?

Explicitly states when to use it: 'Call this first when you need to know the temperature, wind, humidity or precipitation limits...' and also covers the alternate use case 'to enumerate all work types in a category.' It adds a strict usage constraint ('Provide either a single work_code or a category, never both') and clarifies the handling of empty results. No ambiguity remains about when to call this tool.

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