Analyse a supplier quote against Costable's should-cost benchmarks. Returns a line-by-line delta (% over/under should-cost), verdict (LOW / COMPETITIVE / HIGH / VERY HIGH), and a list of the top items to challenge in negotiation.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | Quote line items. | |
| countryCode | Yes | Supplier's manufacturing country (ISO 3166-1 alpha-2). | |
| supplierName | Yes | Name of the supplier (for labelling the report). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the behavioral disclosure burden. It effectively communicates the analysis nature of the tool and specifies the exact return shape, including the verdict scale and top challenges. It does not discuss side effects, but 'Analys' strongly implies a non-mutating operation.
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 exactly two sentences: the first states the action and object, the second lists return values. It contains no filler, restates no schema details, and is front-loaded with the core purpose.
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 output schema and no annotations, the description provides sufficient context about inputs, outputs, and intended use. The only minor gap is a lack of explicit mention of edge cases or dependencies on Costable benchmark availability, but this is not critical for correct 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 100%, so the input schema already fully documents all parameters. The description adds context about line-by-line analysis and negotiation output but does not materially enhance parameter understanding beyond the schema, which is the expected baseline.
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 specific action: analyzing a supplier quote against Costable's should-cost benchmarks, and enumerates concrete outputs (delta percentages, verdict, top negotiation items). This distinguishes it from sibling tools like estimate_part or calculate_cost_model by focusing on quote review and negotiation support.
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 implies a clear use case: when an agent has a supplier quote and needs to evaluate pricing against should-cost benchmarks for negotiation. It does not explicitly name alternatives or exclusions, but the context is strong enough to route the agent correctly among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
