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 carries the full burden. It discloses the return fields (id and product_url), which is useful, but does not mention whether the operation is read-only, any auth requirements, or limitations like exact matching or case sensitivity. For a lookup tool, the risk is low, but the behavioral disclosure is incomplete.
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 with the main purpose front-loaded. Each sentence adds value: what the tool does, when to use it, and what it returns. No fluff.
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 absence of an output schema and annotations, the description covers the basic purpose, usage, and return fields. However, it does not explain parameter interactions (e.g., AND vs OR), optionality, or value formats. For a 4-parameter tool with no output schema, more detail is expected to be fully 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 coverage is only 25% (only category has a description). The description merely repeats the parameter names without adding meaning: 'colour name' does not specify accepted formats (e.g., exact match, partial), and max_width_mm/max_height_mm are left as plain numbers without units or constraints. It does not compensate for the missing schema descriptions.
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 ('filter') and resource ('catalogue'), and clearly lists the filter dimensions (category, colour, width, height). It also distinguishes itself from the sibling tool by explicitly naming search_catalog as the alternative 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 states the ideal use case ('when the customer has stated a blind type') and explicitly directs to search_catalog for natural language queries. This provides clear when-to-use and alternative guidance.
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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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.