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

Annotations already declare readOnly/idempotent/non-destructive, yet the description adds substantial context beyond them: null fields and default typing are omitted per row, results are partial and non-exhaustive, prior_weight is confidence not effect size, graph_status is an internal eligibility label not clinical verification, dose is evidence context not a dosing recommendation, and pathway_tags are unvalidated raw metadata. This is exactly the caveat layer an agent needs for safe interpretation.

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 purpose is front-loaded in the first sentence and every clause carries information, but the body is a dense run-on paragraph that mixes field semantics, status handling and caveats, making it harder to scan than it needs to be. No filler sentences, though.

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?

With no output schema, the description carries the full burden of explaining the return shape, and it does so field by field (direction, prior_weight, graph_status, has_verified_support/mixed_evidence, dose, pmids, n_studies/rct_count) plus failure handling via the status field. Nothing needed to call or interpret it correctly is missing.

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%, so the baseline is 3, and the direction enum semantics are already spelled out in the schema. The description does add meaning beyond it by noting the biomarker may also be an Aviado id (the schema only lists name, abbreviation, or LOINC code) and by illustrating the LOINC format with '4548-4', which reduces failed lookups.

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 states a specific verb+resource (supplements that move a biomarker) and immediately scopes it to Aviado's registry-classified entities, explicitly distinguishing it from get_interventions_for_biomarker which covers drugs, research compounds and foods. An agent can route between the two siblings without opening either schema.

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?

It gives explicit when-to-use (supplements only, registry-classified and recommendable), when-not (prescription drugs, research compounds, foods, unknown ids), the named alternative to use in that case, and even a pre-call step ('Check status first: needs_confirmation / no_match means the input did not resolve'). Nothing is left to inference.

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