list_ordersRecent orders with 6-axis status (order / payment / fulfillment / delivery / return / review). Bearer + phone required.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-indexed page number. | |
| limit | No | Page size. | |
| phone | Yes | E.164 phone selecting the customer. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does add useful context: authentication requirement (Bearer + phone) and the 6-axis status. But it does not explicitly state that this is a read-only operation, nor does it explain pagination behavior or what 'recent' means, leaving some ambiguity.
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, compact sentence that conveys the core purpose and a key requirement. It is front-loaded with the most important information, contains no redundant words, and is easily scannable.
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 list tool with full schema coverage, the description is fairly complete: it names the resource, output axes, and the required auth. Minor gaps include not specifying pagination defaults or the timeframe for 'recent', but these are not critical given the tool's simplicity.
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 schema already documents all three parameters with meaningful descriptions (page, limit, phone), and marks phone as required. The description emphasizes that phone is required, which is already in the schema, but adds no additional parameter-level semantics beyond that.
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 title and description clearly state that the tool lists a customer's recent orders, with a specific 6-axis status breakdown (order/payment/fulfillment/delivery/return/review). The scope is unambiguous (customer's orders) and it is distinct from sibling tools like list_products or list_payment_links.
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 the tool is used for retrieving recent orders with status details and states the required auth method (Bearer) and required parameter (phone). However, it does not explicitly explain when to use this tool versus alternatives, nor does it mention any exclusions or alternative tools.
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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Each tool targets a distinct entity or action: cart, customer, deals, brands, orders, payment links, products, and gift cards. There is no overlap or ambiguity between tool purposes.
Tool names follow a consistent verb_noun pattern: get_ for single resources, list_ for collections, and validate_ for a specific action. The one outlier, get_customer_best_deals, still fits the get_ prefix pattern, so it's mostly consistent.
With 8 tools, the set is well-scoped for a customer/catalog read API. Each tool serves a clear purpose and none are redundant.
The surface covers customer info, cart, orders, payments, products, brands, deals, and gift cards, which is comprehensive for a read-only domain. Minor gaps like lacking order details or product-level details are workable, and mutations are clearly out of scope.