Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full disclosure burden. It does valuable work by naming the exact evidence categories returned and by signaling a bounded dataset ('24+ common supplements'), which warns the agent that unlisted supplements will likely miss. It does not say what happens for an unrecognized slug, whether results are static or refreshed, or how sparse coverage may be for newer supplements.

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?

A single sentence with the payload front-loaded and no filler; the enumerated evidence types are the most useful content and appear immediately. Slightly dense — the '24+' qualifier is doing double duty as both scope and coverage warning and could be clearer, but nothing is wasted.

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 read-only lookup with no output schema and no annotations, the description adequately compensates by listing the returned data fields, so the agent knows what to expect back. The remaining gap is failure/edge behavior for unknown supplements, which a caller would reasonably want stated.

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% and the schema already gives slug examples, so baseline is 3. The description adds a small but real constraint beyond the schema: the lookup universe is limited to 24+ common supplements, which tells the agent the input must resolve against a fixed list rather than arbitrary free text.

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 ('look up') plus a specific resource (supplement evidence) and enumerates exactly what comes back: adult RDA, UL, PubMed RCT counts, NIH guidance. Nothing in the sibling list (curtain guides, multivitamin comparison, article search) overlaps, so the agent can route here without ambiguity.

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?

Usage is only implied by the resource name — an agent infers this is the tool for supplement dosing/evidence questions. There is no explicit when-to-use framing, no mention of when a sibling like search_healthstylz_articles would be better, and no statement that this is a read-only reference lookup rather than a search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.