get_product_optionsThe choices before a quote: colors (with hex), sizes with their quantity price ladders, whether sizes are priced differently, quantity step, stock by color and size when tracked, free shipping, lead time and rush availability, and the free-sample link when the product qualifies.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Identify the product by exactly one of slug, id or sku. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful conditional details beyond annotations, such as stock being reported 'when tracked' and the free-sample link appearing 'when the product qualifies', giving the agent accurate expectations about conditional response fields.
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 a single sentence that packs a lot of specific information efficiently and front-loads the core purpose. It is slightly dense with a long comma-separated list, but every item adds value and there is no filler.
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?
With no output schema, the description takes on the burden of explaining return contents, and it does so in substantial detail: colors, sizes, pricing ladders, stock, shipping, lead time, and free-sample eligibility. Combined with the fully documented input schema, an agent has enough context to call the tool correctly.
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%, with the product object's slug/id/sku fields clearly documented in the input schema. The tool description does not need to restate parameter details and adds no new parameter-specific meaning, so the baseline score of 3 is appropriate.
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 identifies the resource (product options) and the exact content returned: colors with hex values, size-based quantity price ladders, stock levels, free-shipping status, lead time, rush availability, and free-sample link. This is specific and distinct from sibling tools like get_imprint_options or quote_price, which concern different resources or purposes.
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 'The choices before a quote' gives clear contextual guidance: this tool is for retrieving product options needed when preparing a quote. It does not explicitly name alternative tools or state when not to use it, but the scope is unambiguous enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.