health

get_interventions_for_biomarker

Find mixed interventions in Aviado's current graph that affect a biomarker, labeled with the exact intervention_type value supplement, food_substance, prescription_drug, research_compound, research_nootropic, medical_compound, or unknown. graph_status is the exact internal proven/suspected eligibility label, not independent clinical validation. Coverage is partial and non-exhaustive; absence is not evidence of no effect. pathway_tags are raw graph metadata, not validated mechanisms, and dose/pathway details are suppressed for non-supplement and unknown rows. Use dose_value with dose_unit; legacy dose_mg is populated only for literal mg rows.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Max results (default 25, max 100)
`biomarker`	Yes	Biomarker name, abbreviation, or LOINC code

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: partial coverage, non-exhaustiveness, internal labels, raw metadata, dose suppression for non-supplements, and legacy field use.

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?

Dense and informative, every sentence adds value. Front-loaded purpose and then specifics. Slightly wordy but efficient overall.

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?

No output schema, but description thoroughly explains return values (intervention types, graph_status, pathway_tags, dose fields) and caveats, providing complete context for a 2-parameter tool.

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

Parameters3/5

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

Schema covers both parameters (biomarker and limit) with descriptions. The description adds context about output rather than parameter details, meeting the baseline for 100% schema coverage.

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?

The description clearly states the tool finds mixed interventions affecting a biomarker, listing specific intervention types, and distinguishes it from sibling tools like get_supplements_for_biomarker which are narrower.

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?

Provides guidance on partial coverage and data semantics (graph_status, pathway_tags, dose handling), but does not explicitly compare with siblings or state when to prefer this over similar tools.

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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TDQS

A4.2/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: searching for supplements or conditions, retrieving supplement info, interactions, biomarker mappings, top brands. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent 'verb_noun' pattern with verbs 'get_' and 'search_', and nouns are appropriately plural or singular.

Tool Count5/5

8 tools is well-scoped for a health supplement knowledge base, covering core queries without unnecessary complexity.

Completeness5/5

The tool set covers search, information retrieval, interactions, biomarker mapping, and brand quality, leaving no obvious gaps for a read-only reference server.

Resources

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