Get comprehensive information about a supplement: what it is, what it does, which biomarkers it affects, mechanism of action, and safety contraindications.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It lists what information is returned but does not mention read-only nature, required naming conventions, error handling, or any potential side effects, leaving important behavioral traits undisclosed.
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 action and concisely enumerates the content areas. Every phrase earns its place with no redundancy or filler.
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 one-parameter lookup tool with no output schema, the description covers the key aspects of the returned information. It does not explain behavior for missing supplements or exact-name requirements, but these are minor given the tool's simplicity and clear scope.
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 coverage is 100% with 'Supplement name', and the description does not add beyond that. The baseline of 3 applies because the schema already fully documents the parameter, and the description provides no additional semantic nuance like common vs. scientific name.
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 a specific verb 'Get' and clearly lists the scope of information: what it is, what it does, biomarkers, mechanism, and safety contraindications. This distinguishes it from sibling tools like get_biomarkers_for_supplement or get_supplement_interactions, which target narrower aspects.
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 for a comprehensive overview, but it does not explicitly state when to use this tool over specialized siblings. There is no 'when-not' guidance or named alternatives, leaving the agent to infer the distinction.
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.