list_storesEvery storefront in the catalog, with how many add-ons we track there, how many are discounted right now, and whether its prices include VAT. Use it to answer "which stores do you actually cover" and to get the store slug vocabulary for search_addons.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | Yes | ||
| count | Yes | ||
| stores | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds meaningful behavioral context beyond annotations by describing the output contents (tracked add-on counts, current discounts, VAT inclusion). It does not mention ordering or pagination, but the existence of an output schema reduces the need for that detail.
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 two sentences, front-loaded with the core output and purpose, with no filler. Every clause contributes to understanding what the tool does and when to use it.
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 zero-parameter, read-only list tool with an output schema and strong annotations, the description is complete. It tells the agent what data is returned, why to call it, and how it supports search_addons. Nothing critical is missing 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?
The tool has zero parameters, so there is nothing for the schema to document. The description adds value by hinting at the 'store' slug vocabulary used by search_addons, giving the agent a sense of the identifier format even without a parameter. Baseline 4 is appropriate here.
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 tool as returning 'every storefront in the catalog' with specific details (add-on counts, discounts, VAT). It also names concrete use cases ('which stores do you actually cover' and getting 'store' slug vocabulary), which distinguishes it from sibling tools that focus on add-ons, deals, or prices.
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 explicitly states when to use the tool: to answer store coverage questions and to obtain the 'store' slug vocabulary for search_addons. While it doesn't explicitly mention alternatives or when not to use it, the purpose is clear enough for an agent to select it for store-related queries.
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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