Search Australian grocery products across Woolworths, Coles, Aldi, and Harris Farm. Returns product name, price, brand, category, unit price, and whether it's on special.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | relevance | |
| limit | No | Number of results (1-50, default 20) | |
| query | Yes | Search query (e.g., 'full cream milk', 'chicken breast') | |
| category | No | Filter by category | |
| retailer | No | ||
| on_special | No | If true, only return products on special |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It explicitly states the return fields (name, price, brand, category, unit price, special status) and lists the four covered retailers. It does not mention pagination or rate limits, but for a read-only search this is adequate.
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 short sentences with no filler. It front-loads the action and directly communicates the returned data, making every word useful.
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 6 parameters and no output schema, the description covers the core purpose, scope, and return fields. It omits edge cases like pagination or default sort behavior, but those are derivable from the schema or not critical for a search 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?
Schema description coverage is 67%, so the description should partially compensate. It adds context about retailers and specials, aligning with the retailer and on_special params, but does not explain sort or limit beyond what the schema already provides.
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 'Search Australian grocery products across Woolworths, Coles, Aldi, and Harris Farm' with a clear verb and resource scope. It also lists return fields, distinguishing it from sibling tools like get_product or get_specials.
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 implies use for general product searches across multiple retailers but does not explicitly mention when to use this vs batch_search, compare_prices, or get_cheapest. No alternatives are named.
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.
batch_search, search_products, and get_cheapest all provide product search with overlapping output, and build_basket/save_basket both handle basket creation. However, detailed usage notes (mandatory vs. advanced) help clarify, though an agent could still misselect among the search tools.
All tools follow a consistent verb_noun pattern in lowercase_snake_case (e.g., batch_search, compare_prices, get_product), making the API predictable and easy to learn.
10 tools is well within the ideal 3-15 range and appropriately scoped for a grocery price-comparison and basket-building service, covering search, comparison, history, specials, and basket workflows without redundancy.
The tool set covers the full grocery shopping lifecycle: searching products, comparing prices by barcode, viewing price history, checking specials, building baskets, and saving manually curated selections. No obvious gaps for typical user requests.