Find the highest-quality brands for a supplement, ranked by ProofMark (Aviado's quality score: third-party testing, certifications, and quality metrics).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (max 20) | |
| supplement | Yes | Supplement name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It explains the ranking methodology (ProofMark: third-party testing, certifications, quality metrics), which adds useful context. However, it does not disclose return format, edge cases (e.g., empty results), or any potential limitations, leaving some gaps for a tool without an output schema.
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 a single, well-structured sentence that front-loads the tool's purpose and key ranking criterion. It contains no redundant words or filler, earning a high score for conciseness.
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 list tool with well-documented parameters, the description covers the essential purpose and ranking rationale. It does not describe the return structure, but given the tool's simplicity and the absence of an output schema, the description is largely complete. A brief mention of output format would push it to 5.
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 100% coverage with descriptions for both parameters: 'supplement' as 'Supplement name' and 'limit' as 'Number of results (max 20)'. The description adds no additional meaning or syntax details beyond what the schema already documents, so it meets the baseline for schema-covered parameters.
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: 'Find the highest-quality brands for a supplement'. The specific verb 'Find' and resource 'brands for a supplement' distinguish it from sibling tools like get_supplement_info or get_supplements_for_condition. The ranking metric (ProofMark) is explicitly explained.
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 when to use this tool: when you need top-ranked brands for a specific supplement. It does not explicitly name alternatives or exclusions, but the context from sibling tool names (e.g., get_supplements_for_condition, get_biomarkers_for_supplement) helps clarify its specific niche.
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.
Most tools target distinct resource-action pairs, but get_supplements_for_biomarker and get_interventions_for_biomarker overlap in purpose (both return things affecting a biomarker), with the key difference being intervention type filtering. This could cause misselection if the agent does not read descriptions carefully.
All tools follow a consistent verb_noun pattern using snake_case (get_* and search_*). Naming is predictable and clearly groups resource types, with no mixed conventions or vague verbs.
With 9 tools, the set is well-scoped for a supplement and biomarker information server. Each tool addresses a distinct functional need without redundancy, falling comfortably in the ideal 3-15 range.
The tool surface covers the primary workflow: searching supplements/conditions, retrieving supplement details and biomarkers, finding supplements/interventions for biomarkers, checking interactions, and getting brand quality data. Minor gaps exist, such as no direct biomarker search or tool to get conditions for a supplement, but core use cases are covered.