Run the cost model across multiple manufacturing countries simultaneously to identify the optimal sourcing location. Returns P50/P80/P90 per country and % delta vs baseline.
| Name | Required | Description | Default |
|---|---|---|---|
| modelId | Yes | Cost model ID returned by create_cost_model. | |
| countries | Yes | Array of ISO 3166-1 alpha-2 country codes to compare, e.g. ['US','CN','VN','MX','DE']. Maximum 6. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the disclosure burden. It clearly states that the tool runs the cost model and returns P50/P80/P90 per country with a percentage delta vs baseline, giving the agent a solid picture of the output behavior.
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 purpose; the second states the return values. Every word contributes meaning.
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 two-parameter tool with no output schema, the description covers the core invocation context and the main return shape. The only minor gap is that 'baseline' is not defined, but this does not prevent an agent from correctly selecting and invoking the tool.
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%, so the schema already fully documents both modelId and countries, including an example and maximum length for countries. The description adds little parameter-level meaning beyond reinforcing that countries are the comparison dimension.
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 action — running the cost model across multiple manufacturing countries simultaneously — and names the output (P50/P80/P90 per country and % delta vs baseline). This clearly distinguishes it from siblings like calculate_cost_model by emphasizing the multi-country comparison use case.
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 provides clear context: use this tool when you need to compare multiple manufacturing countries and identify the optimal sourcing location. It does not explicitly mention when not to use it or name an alternative, but the purpose clause is concrete enough for an agent to infer when it applies.
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.
