Find the cheapest option for a product across all Australian grocery retailers. Searches by name and returns results sorted by price ascending.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (1-50, default 10) | |
| product_name | Yes | Product to search for (e.g., 'full cream milk 2L') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that it 'searches by name' and 'returns results sorted by price ascending,' which conveys read-only search behavior and result order. However, it does not mention edge cases like no results, handling of limits, or any other side effects, leaving some ambiguity.
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 sentences, front-loaded with the main purpose, and contains no redundant or filler content. Every word adds value.
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 simple two-parameter search tool with no output schema, the description covers the core purpose, search method, scope, and output sorting. It is sufficiently complete for an agent to invoke correctly, though it could optionally state what fields appear in the results.
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 100% with both 'product_name' and 'limit' well-documented. The description adds context by saying 'by name' and 'price ascending,' but it does not provide additional meaning beyond the schema. Baseline 3 is appropriate.
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 'Find' with a clear resource: 'cheapest option for a product across all Australian grocery retailers.' It also distinguishes the tool from siblings like compare_prices or search_products by explicitly stating the 'cheapest' across all retailers and sorting by price ascending.
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 implies when to use this tool: when you need the cheapest option for a product by name across Australian grocery retailers. It does not explicitly mention alternatives or exclusions, but the context is unambiguous enough for an agent to decide.
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.