Compare prices for the same product across retailers using its barcode. Returns all retailers stocking the product, sorted cheapest first. About 7,800 products have barcode matches.
| Name | Required | Description | Default |
|---|---|---|---|
| barcode | Yes | EAN-13 barcode (e.g., '9300675016902') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the output behavior (all retailers, sorted cheapest first) and provides a critical limitation ('About 7,800 products have barcode matches'), warning the agent about potential empty results. It does not mention pagination or errors, but for a simple lookup 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?
Two sentences, front-loaded with the core purpose immediately, and zero redundant words. It informs the agent about the action, result, and a key constraint efficiently.
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?
The tool is simple (one input, no output schema), and the description covers the purpose, output behavior, and a coverage limitation. It could be more explicit about the exact structure of returned retailers (e.g., names/prices), but given the lack of output schema, 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?
The schema description covers 100% of the parameter (barcode) including format and example. The tool description only references 'barcode' without adding new semantics, so it meets the baseline but does not exceed it.
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 ('Compare') and resource ('prices for the same product across retailers') along with the input method ('using its barcode'). It clearly distinguishes from siblings like get_cheapest (single result) and search_products (text search) by focusing on cross-retailer comparison.
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 clearly indicates when to use it: when you have a barcode and need price comparison across retailers. It also implies the scope ('all retailers stocked, sorted cheapest first'), but does not explicitly mention alternatives or when not to use it, leaving some room for interpretation.
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.