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create_customer

Add new customers to your subscription billing system by providing essential contact details and optional business information for e-commerce platforms.

Instructions

Create a new customer. POST /customers. Required: firstName, lastName, email. Optional: businessName, locale, phoneNum, phoneExt, preferredCurrency, taxExempt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
firstNameYesCustomer first name (required)
lastNameYesCustomer last name (required)
emailYesCustomer email (required)
businessNameNoBusiness name
localeNoLocale code
phoneNumNoPhone number
phoneExtNoPhone extension
preferredCurrencyNoPreferred currency code
taxExemptNoWhether customer is tax exempt
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states 'POST /customers', implying a write operation, but lacks critical behavioral details: it doesn't mention authentication requirements, rate limits, error conditions, whether the operation is idempotent, or what happens on success (e.g., returns a customer ID). For a mutation tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the purpose, the second lists parameters. It's front-loaded with the core action and avoids unnecessary words. However, it could be slightly more polished by integrating the HTTP method more naturally.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks behavioral context (e.g., side effects, error handling), doesn't explain the return value, and provides minimal usage guidance. Given the complexity of creating a customer in a billing system, more detail on prerequisites and outcomes is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 9 parameters with their types and requirements. The description adds minimal value by listing required vs. optional parameters, but doesn't provide additional context like format constraints (e.g., email validation) or business rules beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new customer') and resource ('customer'), making the purpose immediately understandable. It distinguishes from siblings like 'update_customer' or 'delete_customer' by specifying creation. However, it doesn't explicitly differentiate from other creation tools (e.g., 'create_invoice', 'create_product') beyond the resource name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether a customer must not already exist), compare to similar tools like 'update_customer', or specify use cases. The agent must infer usage from the name and context alone.

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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