get_customerReturn tags, ℳ-credit balance, M Club tier for the customer with the given E.164 phone. Bearer required.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | E.164 phone number, e.g. +972501234567. |
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?
No annotations are provided, so the description carries the full burden. It discloses the auth requirement ('Bearer required') and the return fields, but doesn't explicitly state read-only behavior, error handling, or other side effects. This is partially transparent but leaves 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?
Two concise sentences, front-loaded with the main action and returning the specific data fields. Every word adds value, and the structure is immediately 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 single-parameter lookup with no output schema, the description adequately covers what the tool returns and the auth requirement. It doesn't describe response format or errors, but the tool's complexity is low, making this sufficient.
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%, so the baseline is 3. The description repeats the E.164 requirement but adds no new information about the phone parameter beyond what the schema already provides.
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?
Description explicitly states it returns specific customer attributes (tags, ℳ-credit balance, M Club tier) for a customer identified by phone. This clearly distinguishes it from sibling tools like get_customer_best_deals and list_orders.
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 clear context for when to use the tool: when you need customer profile details by phone number. It implies the use case without explicitly listing alternatives or exclusions, making it a clear but not fully explicit guideline.
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.