get_customer
Retrieve detailed information for a specific customer using their email or code.
Instructions
Get details of a specific customer by email or code
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| email_or_code | Yes |
Retrieve detailed information for a specific customer using their email or code.
Get details of a specific customer by email or code
| Name | Required | Description | Default |
|---|---|---|---|
| email_or_code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'get details,' which implies a read operation, but it does not describe response format, error behavior, permissions, or whether any side effects exist. This lack of context is a significant gap for a tool with no annotation support.
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, front-loaded sentence with no unnecessary words. It immediately states the action and the parameter's role, achieving high efficiency.
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 get tool with one parameter and no output schema, the description is minimally adequate. It tells the agent what to expect in terms of action and identifier, but it does not describe the return payload or potential error cases. Given the tool's simplicity, this is a moderate gap but not severe.
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 adds meaning by clarifying that the single parameter 'email_or_code' can be either an email address or a customer code, which is helpful. However, it does not specify formats, examples, or validation rules beyond the schema's basic string type.
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: retrieving details for a specific customer, identified by email or code. The verb 'get' and resource 'customer' are specific, and the identification method distinguishes it from sibling tools like list_customers and get_product.
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 appropriate usage: when you need details for one specific customer and have their email or code. It does not explicitly mention alternatives like list_customers, but the context is unambiguous enough for an agent to infer when to use it.
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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