Retrieve a confirmed order's status, items, and payment details by order_id. customer_email is required as soft-auth and must exactly match the order record — prevents arbitrary order lookups. Returns payment status (paid | pending | failed), production status, and dispatch date once available.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | ||
| customer_email | 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 explains the soft-auth mechanism, the anti-lookup safeguard, and the return fields including payment status, production status, and dispatch date. This is rich behavioral context beyond what the schema provides.
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 two sentences long, front-loaded with the primary purpose and followed by necessary constraints and return details. Every sentence contributes value with no redundancy.
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 read operation with 2 parameters and no output schema, the description covers the key return values (payment status, production status, dispatch date) and the auth constraint. It does not explicitly outline alternative tools for non-confirmed orders, but the context signals are sufficient for a low-complexity tool.
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?
With 0% schema description coverage, the description compensates by explaining order_id as the identifier and customer_email as a soft-auth field that must match the order record. This adds meaningful semantics beyond the bare string type definitions, though it leaves basic formatting to be inferred.
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 retrieves a confirmed order's status, items, and payment details, using a specific verb and naming the resource. It also distinguishes this tool from siblings like get_cart and get_checkout by specifying 'confirmed order'.
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 specifies that customer_email is required and must exactly match the order record, which is a clear prerequisite for using the tool. It does not explicitly name alternatives or exclusions, but the context of confirmed orders and the soft-auth requirement gives clear usage context.
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.
Most tools have distinct purposes, but the presence of three search tools (search_catalog, search_products, lookup_catalog) and legacy tools (create_order, submit_enquiry) creates minor overlap. Descriptions help differentiate, but an agent might initially confuse them.
All 26 tool names follow a consistent verb_noun snake_case pattern (e.g., cancel_cart, configure_product). No mixed cases or irregular verbs are present.
26 tools is slightly high for a blind shop, but the domain requires many operations (search, pricing, cart, checkout, recommendations, etc.). Some legacy tools could be removed, but the count is still reasonable.
The tool surface covers the full customer journey from product discovery (search, recommendations) to purchase (cart, checkout, payment) and post-purchase (orders, swatches, price match, delivery estimates). No obvious gaps for the stated purpose.