Compare prices for a specific product across multiple merchants.
Finds the same product at different retailers and shows price differences.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | No | Optional session identifier to link tool calls in one conversation. | |
| product_name | Yes | Specific product to compare prices for, e.g. 'Nike Pegasus 41' |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that it finds the same product and shows price differences, but does not mention data freshness, merchant coverage, or whether it performs live lookups. Basic behavior is clear but not deeply disclosed.
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 concise sentences that lead with the main purpose and add a clarifying second sentence. No unnecessary detail or repetition.
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 comparison tool with a full output schema and well-documented parameters, the description is largely sufficient. It could be more complete with explicit usage guidance or alternative tool references, but overall it supports correct selection and invocation.
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 already provides full descriptions for both parameters, including an example for product_name. The description adds little beyond the schema, so the baseline score of 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 clearly states the tool compares prices for a specific product across multiple merchants and shows price differences. This specific verb-resource pairing distinguishes it from sibling tools like search_products and find_deals.
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 intended context is clear: use when you want to compare the same product's prices at different retailers. However, it does not explicitly mention when not to use it or name alternatives such as find_deals or search_products.
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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The tools are mostly distinct: search_products and find_deals overlap somewhat (both search), and compare_prices overlaps with search_products in returning comparison data, but the descriptions clarify the different intents. The checkout lifecycle tools are clearly separated.
All tool names follow a consistent verb_noun snake_case pattern (check_order, compare_prices, complete_purchase, find_deals, search_products, start_checkout, update_checkout_info). No mixed conventions or vague verbs.
Seven tools is well-scoped for a shopping assistant covering search, deals, comparison, and the full checkout flow. Each tool earns its place without redundancy.
The toolset covers the core shopping lifecycle: discovery (search, deals, compare), checkout (start, update, complete), and order status (check). Minor gaps exist such as no explicit cancel checkout or detailed order retrieval, but agents can work around these.
