add_to_cart
Storefront: add a billing variant (e.g. coil-scanner-monthly). One licence per person, quantity 1.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| quantity | No | ||
| product_id | Yes |
Storefront: add a billing variant (e.g. coil-scanner-monthly). One licence per person, quantity 1.
| Name | Required | Description | Default |
|---|---|---|---|
| quantity | No | ||
| product_id | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds a meaningful behavioral rule ('one licence per person, quantity 1') and reveals that this is a mutation (adding to cart). However, it does not mention idempotency, whether adding an existing item increments quantity or errors, cart persistence, authentication, or side effects.
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 very concise: two short sentences with no wasted words. The storefront context and example are front-loaded, and the key constraint is stated clearly. It earns its place with actionable information.
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 simple two-parameter mutation tool, the description is minimally adequate: it names the action, gives an example product ID, and constrains quantity. But with no annotations and no output schema, it could usefully state what happens after adding (e.g., whether the item goes straight to a specific cart, whether success or error is returned, or whether duplicates are merged). That would make it fully complete for an agent.
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 0%, so the description must compensate. It partially does: it gives an example of a product_id value and states that quantity should be 1. However, it does not explicitly explain what product_id represents beyond the example, nor does it describe how quantity behaves if omitted or supplied.
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 tool's function: adding a billing variant to a storefront cart, with a concrete example ('coil-scanner-monthly'). It distinguishes itself from the sibling tools like remove_from_cart and update_cart_item by the explicit 'add' action, though it does not name or directly contrast those alternatives.
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 a storefront context and a quantity constraint ('One licence per person, quantity 1'), but it does not explain when to choose this tool over remove_from_cart, update_cart_item, or checkout_handoff. No explicit when-to-use or when-not-to-use guidance is given.
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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