Search for a health condition by name with fuzzy matching. Use this to resolve ambiguous condition names before calling get_supplements_for_condition.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., 'sleep', 'anxiety', 'brain fog', 'joint pain') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses that the tool uses fuzzy matching and is meant for disambiguation, but it does not mention result format, error behavior, or whether the operation is read-only. This is adequate for a simple search tool, but additional behavioral details would improve transparency.
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 concise and front-loaded. The first sentence states the action, the second provides contextual usage. Every sentence adds value with no 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 tool with one parameter and no output schema, the description is mostly complete but lacks explicit information about return values. It says it searches and resolves ambiguity, but does not state what the agent will receive, which is a notable gap given there is no output schema.
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% as the query parameter has a description with examples. The tool description does not add further semantics beyond 'by name,' but given full schema coverage, a baseline score 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 the tool's function: 'Search for a health condition by name with fuzzy matching.' It also distinguishes from siblings by explicitly mentioning the intended use case of resolving ambiguous condition names before calling get_supplements_for_condition, providing a specific verb and resource.
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 gives explicit usage guidance: 'Use this to resolve ambiguous condition names before calling get_supplements_for_condition.' This clearly tells the agent when to use this tool and names a specific sibling as the follow-up, effectively distinguishing it from alternatives.
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.