get_productLegacy commerce tool. Retrieve product-quality, certification, price, availability, and affiliate purchase data. Use get_evidence_summary for clinical evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the 'Legacy' deprecation context and the list of returned data categories, which is useful but stops short of behavioral details like response format, pagination, or error behavior.
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 short sentences front-load the legacy status and core retrieval action, followed by a useful pointer to an alternative. No filler or redundancy.
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 retrieval tool with one parameter and no output schema, the description conveys the returned data scope and points to the main alternative, while annotations cover side effects. It lacks details like response format or edge cases, but is reasonably complete for its simplicity.
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 0% and the description does not mention product_id at all, leaving the parameter semantics entirely to the schema. Although product_id is self-explanatory and the only required parameter, the description fails to compensate for the lack of schema documentation.
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 the verb 'Retrieve' with a specific resource scope: product-quality, certification, price, availability, and affiliate purchase data. It clearly distinguishes itself as a commerce tool from clinical evidence tools like get_evidence_summary.
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?
It provides an explicit when-not alternative: 'Use get_evidence_summary for clinical evidence.' However, it does not offer guidance for choosing between this and other commerce siblings like get_price_history or search_supplements, and 'Legacy' only weakly implies usage context.
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 evidence tools (compare_evidence, get_citations, get_evidence_summary, query_evidence_map, search_evidence) and commerce tools (compare_supplements, get_price_history, get_product, recommend_for_goal, search_supplements) are clearly separated, but a few pairs like compare_evidence vs compare_supplements and search_evidence vs query_evidence_map could cause confusion despite different data sources being described.
All tool names follow a consistent verb_noun snake_case pattern (e.g., search_evidence, get_citations, compare_evidence), making it easy to predict functionality from the name.
The 10 tools are within a reasonable range, but the legacy commerce tools (compare_supplements, get_price_history, get_product, recommend_for_goal, search_supplements) add redundancy and could be trimmed without losing core functionality, making the set slightly over-sized.
The evidence surface fully covers search, summary, comparison, citation retrieval, and dataset querying, while the commerce tools provide complete product lookup, price history, and recommendation capabilities. No major gaps are apparent for the stated purpose.