recommend_for_goalLegacy commerce tool. Returns catalog/product rankings and may contain affiliate URLs. Rankings are not clinical-evidence grades or personalized advice.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| limit | No | ||
| budget_usd | No | ||
| demographic | No | general |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations by disclosing that the tool 'may contain affiliate URLs' and that rankings are not 'personalized advice'. Given readOnlyHint and idempotentHint are true and destructiveHint is false, the safety profile is already clear; the description enriches understanding of the tool's commercial nature and limitations.
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 with no filler. It front-loads the legacy status and core purpose, then adds key caveats. Every sentence earns its place, and the structure is easy to parse quickly.
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 has no output schema and 4 parameters, so the description should ideally describe return format and parameter interactions. It partially does by stating it returns 'catalog/product rankings' and mentions affiliate URLs, but it lacks details on response structure or how demographic/budget influence results. For a legacy tool with potential bias, more context would be valuable, but the core purpose is clear enough for basic 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?
With 0% schema description coverage, the description provides no explanation of the parameters (goal, limit, budget_usd, demographic). It only mentions 'catalog/product rankings', which hints at goal-based ranking but does not elaborate on how limit, budget, or demographic affect results. The schema provides defaults and enum values, so some semantics exist, but the description fails to compensate for the coverage gap.
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's function: 'Returns catalog/product rankings' and explicitly notes 'may contain affiliate URLs'. It distinguishes itself from sibling evidence tools by stating rankings are 'not clinical-evidence grades or personalized advice', which differentiates it from search_evidence, get_evidence_summary, and compare_evidence.
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?
No explicit guidance on when to use this tool versus alternatives is provided. The word 'Legacy' implies it may be outdated, but it does not specify use cases, exclusions, or recommend another sibling tool for better options. The only hint is the 'not clinical-evidence grades' caveat, which implies not to use it for clinical decisions, but this is not practical usage guidance.
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.