Register as a GYOTAK customer. Collect name, phone, address, email, and customer type (Retail or Wholesale) via conversation BEFORE calling this tool. Returns a customerKey for future orders. Save the key in your AI memory.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Full name (English) | |
| No | Email address (optional) | ||
| phone | Yes | Contact phone number | |
| address | Yes | Shipping address | |
| customerType | Yes | Customer type: Retail (consumer) or Wholesale (business) |
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, the description carries the behavioral disclosure burden. It reveals that the tool returns a customerKey and instructs saving it in AI memory, which is key for future orders. It does not mention potential failure modes or duplicate registration behavior, but covers the essential workflow.
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?
Three short sentences with no filler. The purpose, prerequisite, and return-value handling are each stated once, efficiently and in logical order.
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?
The description explains what to collect, what will be returned, and how to handle the result, which is sufficient for a registration tool with no output schema. It does not cover edge cases like validation errors or duplicate registrations, but those are not critical for basic usage.
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 coverage is 100%, so the description doesn't need to explain parameter meanings. The description lists the same fields as the schema and adds no new semantics beyond the conversational collection requirement.
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 states a clear action: 'Register as a GYOTAK customer.' It identifies the resource (customer) and differentiates itself from siblings like place_order and recover_customer_key by focusing on customer creation.
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 explicitly tells the agent to collect all required information via conversation BEFORE calling, which gives a clear prerequisite. It does not mention alternatives like recover_customer_key for already-registered customers, so some usage context is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clear role in the customer, ordering, payment, or verification flow. The only potential confusions (get_catalog vs search_fish, get_catch_reports vs verify_catch) are explicitly differentiated in their descriptions.
All tools follow a consistent verb_noun snake_case pattern using verbs like get, create, place, verify, check, and ask. Brand-specific tools like ask_gyotak and contact_gyotak still fit the same convention.
18 tools is slightly on the heavy side for a single server, but the scope spans sales, payments, customer management, and blockchain traceability, so most tools have distinct jobs. It is above the ideal range but not bloated.
The core commerce loop (register, order, pay, confirm) and traceability verification are covered well. Missing order cancellation, guest order status retrieval, or customer profile editing are minor gaps that agents can usually work around.