Verify if a supplement matches a record in the NIH Dietary Supplement Label Database (DSLD). Returns verification status, DSLD ID, and confidence level. Use this when a user asks if a supplement is legitimate or wants to verify label accuracy.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name to verify against NIH DSLD. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the disclosure burden. It usefully discloses that the tool 'Returns verification status, DSLD ID, and confidence level', but does not describe behavior for edge cases (e.g. no match found) or any data-source semantics beyond matching.
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 tight sentences: purpose, return values, and usage context, all front-loaded with no filler. Every sentence earns its place.
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 single-parameter tool with full schema coverage, the description is complete: it covers what it does, what it returns, and when to use it. Minor gap is the absence of edge-case or 'no record found' behavior, which would normally be covered by an 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 description coverage is 100% and the single 'supplement' parameter is already described as the name to verify. The description adds no extra meaning beyond the schema, matching the baseline for full coverage.
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?
States a specific verb ('Verify') and resource ('NIH Dietary Supplement Label Database'), and names the output briefly. The sibling tools cover safety, timing, and quality, so this verification-against-a-database purpose is clearly distinct from them.
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 when a user asks if a supplement is legitimate or wants to verify label accuracy.' It provides clear when-to-use context, though it does not state exclusions or name an alternative tool as a fallback.
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.
check_supplement_safety overlaps heavily with get_supplement_timing, grade_supplement_quality, and verify_nih_label by returning timing, quality grade, filler detection, and NIH verification alongside safety data. This creates unclear boundaries and makes tool selection ambiguous.
All tool names follow a consistent verb_noun snake_case pattern: check_full_stack, check_supplement_safety, get_supplement_timing, grade_supplement_quality, verify_nih_label. The naming is predictable and easy to navigate.
Five tools is a well-scoped size for the supplement advisory domain. Each tool covers a distinct high-level concern, and the count is neither bloated nor too thin.
The tool set covers full-stack interaction checking, individual safety checks, timing, quality grading, and NIH label verification. Minor gaps exist around detailed supplement information retrieval, but the core domain is functionally complete.