Aviado Health BioIntelligence

Supplements for a condition

get_supplements_for_condition

Read-onlyIdempotent

Supplements associated with a health condition or goal (e.g. 'insomnia', 'anxiety', 'joint pain'), strongest evidence first. evidence_basis direct rows come from studies of the condition; biomarker_proxy rows are inferred from the supplement's effect on the named proxy_biomarkers (each backed by an effect the Aviado app shows), are hypotheses, and are graded at most D. Only recommendable supplements are listed by default (rows carry intervention_type, entity_type, regulatory_status and recommendable only where they differ from coverage.row_defaults; null fields are omitted); include_non_supplements lists prescription drugs and research compounds too, labelled, as context only. dose is a typical study dose {value, unit}: evidence context, not a dosing recommendation (coverage.dose_note). Partial: absence is not evidence a supplement does not help. A weak or ambiguous condition name returns needs_confirmation with candidate conditions; confirm one and pass its id. Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Max supplements to return, ranked by evidence (default: 25, max: 50)
`condition`	Yes	Health condition, symptom, or goal (e.g., 'insomnia', 'anxiety', 'brain fog', 'joint pain')
`min_grade`	No	Minimum evidence grade to include (default: D = all)	D
`include_non_supplements`	No	Also list prescription drugs, research compounds and other non-recommendable entities, labelled (default false)

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 13, 2026

addedInput schema / properties / include_non_supplements

Added value: +{
+  "default": false,
+  "description": "Also list prescription drugs, research compounds and other non-recommendable entities, labelled (default false)",
+  "type": "boolean"
+}

Changed2 schema fields changedJul 20, 2026
- addedInput schema / properties / condition / maxLength
```
Added value: +512
```
- addedInput schema / properties / condition / minLength
```
Added value: +1
```
Changed3 schema fields changedJul 1, 2026
- addedInput schema / properties / limit / maximum
```
Added value: +50
```
- addedInput schema / properties / limit / minimum
```
Added value: +1
```
- changedInput schema / properties / limit / type
```
Previous value: -"number"New value: +"integer"
```

Changed1 schema field changedJun 30, 2026

addedInput schema / properties / limit

Added value: +{
+  "default": 25,
+  "description": "Max supplements to return, ranked by evidence (default: 25, max: 50)",
+  "type": "number"
+}

AddedJun 30, 2026

TDQS

A4.6/5.0

Behavior5/5

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 semantics beyond them: biomarker_proxy rows are hypotheses graded at most D, only recommendable supplements appear by default, null fields are omitted, dose is evidence context rather than a dosing recommendation, and partial results do not imply absence of effect. This is exactly the extra behavioral context annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and ranking rule are front-loaded, and nearly every clause carries non-redundant semantics. It is, however, a dense run-on paragraph with nested parentheticals that could be broken into shorter statements without losing content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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 and delivers: it names the row fields (intervention_type, entity_type, regulatory_status, recommendable, coverage.row_defaults), the dose shape {value, unit}, the evidence_basis categories, and the error/confirmation envelope. An agent has everything needed to call and interpret it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: include_non_supplements is described as surfacing labelled prescription drugs and research compounds 'as context only', and condition resolution behavior (ambiguous input -> needs_confirmation with candidate ids) is documented beyond the schema's simple string definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Supplements associated with a health condition or goal') plus the ranking rule ('strongest evidence first') and the evidence_basis split that separates direct rows from biomarker_proxy rows. This lets an agent distinguish it from the sibling get_supplements_for_biomarker 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.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear operational context: check status first, needs_confirmation/no_match means the input did not resolve and next_step tells you how to retry, and confirm a candidate condition then pass its id. It also explains when to set include_non_supplements. It stops short of naming sibling alternatives (e.g. search_conditions) explicitly, so it is clear context rather than a full when/when-not routing guide.

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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