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create_customer

Add new customers to your GoCardless account by providing their email, name, and optional company details for payment processing.

Instructions

Create a new customer in GoCardless

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesCustomer email address
given_nameNoCustomer first name
family_nameNoCustomer last name
company_nameNoCustomer company name (optional)

Implementation Reference

  • Handler function for the 'create_customer' tool. Constructs parameters from input arguments and calls client.customers.create() to create a new customer in GoCardless.
    elif name == "create_customer":
        params = {
            "email": arguments["email"],
        }
        if "given_name" in arguments:
            params["given_name"] = arguments["given_name"]
        if "family_name" in arguments:
            params["family_name"] = arguments["family_name"]
        if "company_name" in arguments:
            params["company_name"] = arguments["company_name"]
    
        customer = client.customers.create(params=params)
        return [
            types.TextContent(
                type="text",
                text=f"Customer created successfully:\n{_format_json({'id': customer.id, 'email': customer.email})}",
            )
        ]
  • Registration of the 'create_customer' tool in list_tools(), including its name, description, and input schema definition.
    types.Tool(
        name="create_customer",
        description="Create a new customer in GoCardless",
        inputSchema={
            "type": "object",
            "properties": {
                "email": {
                    "type": "string",
                    "description": "Customer email address",
                },
                "given_name": {
                    "type": "string",
                    "description": "Customer first name",
                },
                "family_name": {
                    "type": "string",
                    "description": "Customer last name",
                },
                "company_name": {
                    "type": "string",
                    "description": "Customer company name (optional)",
                },
            },
            "required": ["email"],
        },
    ),
  • Input schema definition for the 'create_customer' tool, specifying required 'email' and optional name fields.
    inputSchema={
        "type": "object",
        "properties": {
            "email": {
                "type": "string",
                "description": "Customer email address",
            },
            "given_name": {
                "type": "string",
                "description": "Customer first name",
            },
            "family_name": {
                "type": "string",
                "description": "Customer last name",
            },
            "company_name": {
                "type": "string",
                "description": "Customer company name (optional)",
            },
        },
        "required": ["email"],
    },
Behavior2/5

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. While 'Create' implies a write/mutation operation, the description doesn't specify whether this requires specific permissions, what happens on duplicate emails, what the response format is, or any rate limits. For a mutation tool with zero annotation coverage, this represents a significant gap in behavioral context.

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

Conciseness5/5

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

The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a straightforward creation tool and gets directly to the point, making it easy for an agent to parse quickly.

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?

Given that this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address what the tool returns, error conditions, authentication requirements, or how it fits into the broader workflow with sibling tools. For a tool that creates persistent data in a payment system, more contextual information would be helpful.

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?

The input schema has 100% description coverage, with all four parameters clearly documented in the schema itself. The description doesn't add any additional parameter information beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 the target system ('in GoCardless'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_customer' or 'list_customers' beyond the obvious creation vs retrieval distinction, which prevents a perfect score.

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 (like authentication requirements), when not to use it (e.g., for updating existing customers), or how it relates to sibling tools like 'list_customers' for verification. This leaves the agent with minimal context for tool selection.

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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