Get manufacturing labour rates ($/hr) for all skill levels in a given country. Data covers 16 skill levels across 25 countries (ILO/BLS/ABS sourced).
| Name | Required | Description | Default |
|---|---|---|---|
| countryCode | Yes | ISO 3166-1 alpha-2 country code, e.g. 'US', 'CN', 'DE', 'VN'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses coverage (16 skill levels, 25 countries) and sourcing (ILO/BLS/ABS), but it does not describe behavior for unsupported country codes, data vintage, or whether rates are averages/medians. This is a meaningful but incomplete disclosure.
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?
Two sentences with no filler. The first sentence front-loads the action and resource; the second adds valuable coverage/sourcing context. Every sentence earns its place.
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 low-complexity tool with one parameter and no output schema, the description covers what is returned, the unit, and the data scope. It could mention the behavior for countries outside the 25 covered, but the core information needed to call it correctly 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?
Schema description coverage is 100% and the single countryCode parameter is already well documented with an ISO example. The description adds no parameter-specific detail beyond restating the country context, so the baseline of 3 applies.
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 ('Get'), a precise resource ('manufacturing labour rates ($/hr)'), and a clear scope ('for all skill levels in a given country'). This distinguishes it from sibling tools like compare_manufacturing_countries and search_materials without needing to inspect schemas.
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 phrase 'in a given country' clearly implies this tool is for single-country labour rate lookups, which separates it from compare_manufacturing_countries. However, it does not explicitly state when not to use it or name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools target a distinct action and resource: search vs. add vs. calculate vs. compare vs. review. The only mild ambiguity is between estimate_part and calculate_cost_model, but their descriptions clearly distinguish a quick natural-language estimate from running a full saved model.
All tool names follow a consistent verb_noun pattern using imperative verbs like create, add, calculate, compare, search, get, and review. This makes the tool surface predictable and easy to navigate.
Ten tools is well-scoped for a should-cost analysis domain. Each tool covers a clear stage in the workflow: model creation, component/process lookup, cost calculation, country comparison, and quote review.
The core should-cost workflow is covered well: create a model, add materials and processes, calculate, compare locations, benchmark quotes, and perform quick estimates. Minor gaps exist around updating or removing model contents and around broader lifecycle management, but agents can work around these.
