Get historical price data for a product. Shows price changes over time with stats (lowest, highest, average). History depth depends on your tier (30-365 days).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days of history (default: max for your tier) | |
| product_id | Yes | Product ID from search results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It discloses the tier-dependent history depth (30-365 days) and the nature of the output, and 'Get' implies a read-only operation. However, it does not explicitly state side-effect-free behavior, permissions, or rate limits, leaving some room for 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 only two sentences long, front-loaded with the primary purpose, and every sentence adds meaningful information—the stats and the tier constraint. There is no redundant or fluff content.
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 read-only tool with two parameters and no output schema, the description covers the purpose, output contents, and an important operational constraint (tier). It does not mention pagination or response format, but those are not essential given the simplicity of the 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?
The input schema provides descriptions for both product_id and days, giving 100% coverage. The description enhances this by adding the specific range '30-365 days' for history depth, which clarifies the schema's 'default: max for your tier' and provides extra semantic value for the days parameter.
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 the verb 'Get' and the resource 'historical price data for a product', distinguishing it from siblings like get_product (current data) and get_cheapest (single best price). It also specifies the output: price changes over time with stats (lowest, highest, average).
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 usage for historical price trend analysis, but it does not explicitly state when to use this tool versus alternatives such as get_specials or compare_prices. There are no exclusion criteria or alternative tool references, leaving usage guidance mostly implied.
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.