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printing-labs

by com.printinglabs
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calculate-bulk-price

Compute the effective bulk price for a given product and quantity: applies the product's quantity-discount tiers and GST, validates the minimum order quantity, and shows the next discount tier. Pass options to price a choice other than the default (e.g. UV printing, which can add an extra per unit). Indicative pricing — final quotes come from the sales team.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesProduct permalink slug or id (from search-products)
optionsNoOptional choices from get-product-details options, e.g. ["Print Type: UV"] (or just ["UV"]). Omit for the defaults.
quantityYesNumber of units

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / options
      Added value: +{
+  "description": "Optional choices from get-product-details options, e.g. [\"Print Type: UV\"] (or just [\"UV\"]). Omit for the defaults.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses MOQ validation, GST application, tier logic, the next-tier preview, that options can add per-unit cost, and flags the result as indicative rather than binding. It doesn't say what happens on MOQ failure (error vs. message) or whether the call is side-effect free.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the core computation, then the options nuance, then the important indicative-pricing caveat. No filler or repetition of the name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description must cover returns, and it names the key outputs (effective price, discount tiers, next tier) plus the indicative caveat. A fuller account of the returned breakdown would make it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so the baseline is 3, but the description adds genuine meaning beyond it: options can alter per-unit price (e.g. UV printing), quantity drives tiering, and slug identifies the product. Syntax examples for options come from the schema, so credit is modest but real.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (compute) and resource (effective bulk price) and enumerates the exact operations performed: quantity-discount tiers, GST, MOQ validation, next-tier display. This is distinguishable from pricing siblings such as get-brand-store-pricing and request-quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context ('for a given product and quantity', 'pass options to price a choice other than the default') and an implicit exclusion via 'final quotes come from the sales team', which routes binding quotes to request-quote. It stops short of naming that alternative explicitly or stating when-not to call.

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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