Verify whether a specific colour is available before configure_product. Returns in_stock boolean, expected_restock_date if out of stock, and up to 5 alternative in-stock colours ordered by similarity. Call this when a customer requests a named colour or when you want to prevent a configure_product failure due to an out-of-stock selection.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes | ||
| colour_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden, and it delivers by disclosing the return payload ('in_stock boolean, expected_restock_date if out of stock, and up to 5 alternative in-stock colours ordered by similarity'). This goes beyond simple mutability and informs the agent of side effects (none implied) and output structure without being verbose.
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?
Two sentences with no filler. The first sentence states the action and return values; the second provides usage triggers. Both earn their place and the description is immediately scannable.
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?
Given the low complexity (2 params) and no output schema, the description fully covers what the tool returns, when to use it, and its role in the workflow. No gaps remain for an agent to guess.
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 0%, and the description does not explicitly define product_id or colour_name. However, the parameter names are self-explanatory, and the description implies that 'colour' corresponds to colour_name and the action is per product. The baseline for low coverage is 3; the description adds minimal semantic value beyond the schema.
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 uses a specific verb ('Verify') and a specific resource ('whether a specific colour is available'), distinguishing this from sibling tools like get_product or configure_product. It clearly states the tool's role as a pre-check for colour availability before product configuration.
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?
Explicit guidance is provided: 'Call this when a customer requests a named colour or when you want to prevent a configure_product failure due to an out-of-stock selection.' This also frames the tool as a prerequisite step, giving clear context for when to invoke it versus alternatives.
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.