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?
Discloses fuzzy matching behavior, but lacks details on return format, pagination, or error handling. With no annotations, description carries full burden; it's adequate but not rich.
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?
Two sentences, no waste. Essential information front-loaded: what it does and when to use it.
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 search tool with one parameter, description covers purpose, usage context, and key behavior. Missing output details, but acceptable given low complexity.
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 covers 100% of the single parameter with a description. The description adds 'fuzzy matching' context, but doesn't significantly extend beyond schema. Baseline 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?
Description clearly states 'Search for a health condition by name with fuzzy matching', specifying verb, resource, and method. It also distinguishes from siblings by indicating it should be used before get_supplements_for_condition.
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?
Explicitly says 'Use this to resolve ambiguous condition names before calling get_supplements_for_condition', providing clear context for when to use this tool versus 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.
Each tool has a clearly distinct purpose: searching for supplements or conditions, retrieving supplement info, interactions, biomarker mappings, top brands. No overlap or ambiguity.
All tool names follow a consistent 'verb_noun' pattern with verbs 'get_' and 'search_', and nouns are appropriately plural or singular.
8 tools is well-scoped for a health supplement knowledge base, covering core queries without unnecessary complexity.
The tool set covers search, information retrieval, interactions, biomarker mapping, and brand quality, leaving no obvious gaps for a read-only reference server.