Get the optimal time to take a supplement and why. Returns slot (morning/bedtime/with food/etc), clinical reasoning, and personalized instruction. Use this when a user asks when to take a supplement or how to build a timing schedule.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name. e.g. 'Magnesium Glycinate' | |
| medications | No | User's medications — affects timing recommendations. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It transparently states that the tool returns a slot, clinical reasoning, and personalized instruction, making it clear this is a read-only advisory operation with no side effects. It does not mention data sources or limitations, but for this query-oriented tool the disclosed behavior is adequate.
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 convey the tool's purpose, what it returns, and when to use it, with no filler. The primary action and outputs are front-loaded, and the usage guidance follows naturally. Every word 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 low-complexity tool with two parameters, no output schema, and no annotations, the description clearly explains return values (slot, reasoning, instruction) and the trigger scenario. An agent has enough to decide when to call it and what to expect back, even without 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%, so the schema already documents both parameters: 'supplement' with an example and 'medications' as affecting timing. The description adds only that the result includes 'personalized instruction,' which indirectly implies the role of medications. The schema does the heavy lifting, so the 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?
The description names a specific action ('Get the optimal time to take a supplement and why'), identifies the resource (a supplement), and enumerates the concrete return components (slot, clinical reasoning, personalized instruction). This clearly separates it from sibling tools focused on safety, quality, label verification, and full-stack checks.
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 explicitly states when to use the tool: 'when a user asks when to take a supplement or how to build a timing schedule.' This gives clear context, though it does not name sibling alternatives or state when not to use it. The guidance is sufficient for most agent routing decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.