Find which biomarkers are affected by a given supplement. Returns direction (up/down), confidence, dose, and health goal mapping.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25, max 100) | |
| supplement | Yes | Supplement name (e.g., 'fish oil', 'curcumin', 'vitamin D3') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the return content (direction, confidence, dose, health goal mapping), which clarifies expected behavior. However, it does not mention behaviors like what happens if the supplement is unknown or whether results are ordered.
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?
A single, compact sentence that conveys purpose and return fields without any filler. Every word contributes to understanding the tool.
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 is simple with two parameters and no output schema. The description covers the return fields (direction, confidence, dose, health goal mapping), which is sufficient for basic use. It lacks edge-case details, but for a lookup tool this is not a critical gap.
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 100%: both 'supplement' and 'limit' are documented with types, defaults, and constraints. The description adds no parameter-specific semantics beyond the schema, so the baseline 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 what the tool does: 'Find which biomarkers are affected by a given supplement.' It uses a specific verb ('find') and a specific resource ('biomarkers affected by a given supplement'). It differentiates from siblings like get_supplements_for_biomarker (reverse direction) and get_supplement_interactions.
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 context: use this tool when you have a supplement and want to know which biomarkers it affects. However, it does not explicitly state when to use it over siblings, nor does it mention the reverse tool get_supplements_for_biomarker or any exclusions.
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.