Structured catalogue filter: narrow by category (roller | venetian | honeycomb | vertical | outdoor), colour name, max_width_mm, or max_height_mm. Ideal when the customer has stated a blind type. Returns matching products with id and product_url. Use search_catalog for open-ended natural language queries.
| Name | Required | Description | Default |
|---|---|---|---|
| colour | No | ||
| category | No | roller | venetian | honeycomb | vertical | outdoor | |
| max_width_mm | No | ||
| max_height_mm | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It communicates that this is a non-destructive read/filter operation and discloses the return fields (id and product_url). It does not mention pagination, auth, or empty-result behavior, but for a simple lookup tool, the core behavior is well covered.
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 three sentences, front-loaded with the tool's purpose, and every sentence adds value. It efficiently covers functionality, ideal usage, and the alternative tool without unnecessary verbosity.
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 tool with four optional parameters and no output schema, the description gives enough context about the filter fields and the response shape. It does not specify behavior for no results or whether filters are combinable, but given the low complexity, it is reasonably complete.
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 description coverage is only 25%, so the description must compensate. It names all four parameters and provides the category enum values, but it does not explain matching semantics for colour name (exact vs partial), the meaning of max_width_mm/max_height_mm beyond their names, or how multiple filters interact. This is adequate but not comprehensive.
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 this is a structured catalogue filter that narrows by category, colour, or dimensions, and returns matching products with id and product_url. It explicitly distinguishes from the sibling search_catalog tool for open-ended queries.
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?
It provides explicit usage guidance: use when the customer has stated a blind type, and use search_catalog for open-ended natural language queries. This gives a clear when-to-use and when-not-to-use directive.
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 clearly distinct purposes, but search_products and lookup_catalog overlap significantly in filtering by category and dimensions, which could confuse an agent. The three search/filter tools have subtle differences described, but the overlap lowers the score from perfect.
All tools use consistent snake_case with verb-noun structure (e.g., cancel_cart, get_product, update_checkout). There is no mixing of conventions, and the verbs clearly indicate the action, making the set predictable and easy to parse.
26 tools is slightly above the typical well-scoped range of 3-15, but the domain of an e-commerce blinds shop with additional services like AR, swatches, and price matches justifies the count. A couple of legacy tools (create_order, submit_enquiry) add minor bloat.
The core purchase flow (search, configure, cart, checkout, payment, order tracking) is fully covered. Additional services like AR visualization, swatches, price match, and WhatsApp handoff are present. Missing features like order listing or coupon application are minor gaps.