Aviado Health BioIntelligence
Server Details
Evidence-based supplement research for AI assistants. Query 4,794 clinical effect edges across 290 supplements and 502 biomarkers, 765 supplement-supplement interactions, 1,634 FDA drug interactions, and 22,570 product quality scores with AviScore ratings. 8 tools: supplement and biomarker lookup, drug and supplement interactions, brand quality rankings, condition-based supplement recommendations, fuzzy search for supplements and health conditions.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.8/5 across 8 of 8 tools scored. Lowest: 3.2/5.
Each tool has a clearly distinct purpose: retrieving supplement info, biomarkers, interactions, brands, and search tools for name resolution. No overlap in functionality.
All tool names follow a consistent pattern: 'get_' for data retrieval and 'search_' for name resolution. No mixing of conventions.
8 tools cover the core domain of supplement and biomarker information without excess. The count is well-scoped for a knowledge base server.
The set provides comprehensive coverage for supplement exploration: search, info, biomarkers, interactions, and brands. Minor gaps like detailed condition information do not hinder core workflows.
Available Tools
8 toolsget_biomarkers_for_supplementAInspect
Find which biomarkers are affected by a given supplement. Returns direction (up/down), confidence, dose, and health goal mapping.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25, max 100) | |
| supplement | Yes | Supplement name (e.g., 'fish oil', 'curcumin', 'vitamin D3') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions the return data but does not state whether the operation is read-only, what authentication is required, any rate limits, or error conditions. For a query tool, the lack of explicit 'read-only' or 'no side effects' is a gap.
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 effectively communicate purpose and return fields. Every sentence adds value, and the structure is front-loaded with the primary action.
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 simple 2-parameter tool with no output schema, the description provides adequate purpose and return fields. However, missing behavioral transparency (no read-only indication) and lack of usage guidance relative to siblings leave gaps.
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%; both parameters have descriptions in the schema. The description adds return field context but does not elaborate on parameter behavior beyond the schema. 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 clearly states the verb 'Find' and the resource 'biomarkers affected by a given supplement', and lists the return fields (direction, confidence, dose, health goal mapping). It is distinct from siblings like 'get_supplements_for_biomarker' which does the reverse.
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 implies usage when one wants to know biomarkers for a supplement, but does not explicitly state when to use this tool versus alternatives like 'get_supplement_info' or 'get_supplements_for_condition'. No exclusion criteria or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplement_infoAInspect
Get comprehensive information about a supplement: what it is, what it does, which biomarkers it affects, mechanism of action, and safety contraindications.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It describes a read operation ('Get') but does not disclose idempotency, rate limits, or response format. It adds no extra context beyond function.
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 description is a single, front-loaded sentence that efficiently conveys the tool's purpose and scope without unnecessary words.
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?
Given the tool's simplicity (one parameter, no output schema), the description is largely complete. It explains what information is returned, though it does not specify error handling or output format.
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% with a single parameter described as 'Supplement name'. The description adds no meaningful semantic detail beyond the schema, such as examples or allowed values.
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 clearly states 'Get comprehensive information about a supplement' and lists specific content areas (what it is, does, biomarkers, mechanism, safety), distinguishing it from sibling tools like get_biomarkers_for_supplement or get_supplement_interactions.
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 implies use for broad overview but does not explicitly state when to use versus siblings or provide when-not-to-use guidance. It lacks explicit context for agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplement_interactionsBInspect
Find supplement-supplement interactions (765 pairs from clinical evidence) and drug-supplement interactions (1,626 FDA-validated pairs). Returns synergies, antagonisms, absorption conflicts, and timing recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| supplement | Yes | Supplement name | |
| include_drug_interactions | No | Include FDA drug-supplement interactions |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral expectations. It mentions the tool returns synergies, antagonisms, etc., and cites evidence sources, but does not disclose error handling, required permissions, rate limits, or what happens with invalid inputs. This lack of transparency is a significant gap.
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, compact and efficient. First sentence states the scope, second lists return types. No unnecessary words or repetition.
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?
Given no output schema, the description adequately hints at return content (synergies, antagonisms, etc.). It covers the main purpose and scope. However, it lacks details on return format, pagination, or handling of missing data, which would be helpful for an agent.
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?
Both parameters have descriptions in the schema (100% coverage), so baseline is 3. The description adds context about clinical evidence and FDA validation, but does not provide additional meaning for the parameters themselves beyond what the schema already offers.
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 clearly states it finds supplement-supplement and drug-supplement interactions, specifying counts and types (synergies, antagonisms, etc.). The name 'get_supplement_interactions' is a specific verb-resource pair. It distinguishes itself from siblings like 'get_supplement_info' by focusing solely on interactions.
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?
No explicit guidance on when to use this tool versus alternatives such as 'search_supplements' or 'get_biomarkers_for_supplement'. The description does not mention when not to use it or suggest other tools for different needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplements_for_biomarkerBInspect
Find which supplements affect a given biomarker (e.g., triglycerides, CRP, vitamin D). Returns evidence-weighted results from 4,794 clinical effect edges.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25, max 100) | |
| biomarker | Yes | Biomarker name, abbreviation, or LOINC code |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It adds value by noting 'returns evidence-weighted results from 4,794 clinical effect edges,' which implies a data source and weighting scheme. However, it does not disclose rate limits, authorization needs, pagination, or whether the tool is read-only (though it is likely safe). The description partially compensates for missing annotations but leaves key behavioral aspects unspecified.
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 description consists of two sentences with no extraneous words. The first sentence front-loads the primary action, and the second provides useful contextual detail (evidence-weighted, number of edges). It is concise and well-structured for an AI agent to grasp quickly.
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?
Given the tool's simplicity (2 parameters, no nested objects, no output schema), the description covers the core purpose and data source. However, it omits details like return format, ordering, how evidence weights are derived, and whether results are paginated. An agent might still need to infer these, making it somewhat incomplete.
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. The description adds context by listing example biomarker values (triglycerides, CRP, vitamin D), which helps clarify acceptable inputs. However, it does not explain the meaning of the 'limit' parameter beyond what the schema already provides. Overall, the description adds modest semantic value to the schema.
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 clearly states the tool's purpose: 'Find which supplements affect a given biomarker' with concrete examples (triglycerides, CRP, vitamin D). The name and description together distinguish it from siblings like 'get_biomarkers_for_supplement' (reverse lookup) and 'get_supplements_for_condition' (condition-based filtering), though no explicit exclusion is provided.
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?
No guidance is given on when to use this tool compared to alternatives (e.g., searching by condition or supplement). There are no explicit 'when-to-use' or 'when-not-to-use' statements, and no prerequisites or context are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplements_for_conditionAInspect
Find which supplements help with a health condition or goal (e.g., 'sleep', 'anxiety', 'joint pain'). Returns evidence-graded supplement recommendations with dosages.
| 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, description carries full burden. It states the tool returns 'evidence-graded supplement recommendations with dosages' and mentions ranking by evidence, which informs the agent of the output structure. However, it could be more explicit about idempotency or side effects.
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, front-loaded with purpose and examples. No wasted words; every sentence provides essential information.
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?
No output schema, but description covers the key return aspects (evidence grades, dosages). Missing details like pagination or exact response format, but sufficient given the tool's simplicity.
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%, but description adds value by providing examples ('sleep', 'anxiety') for condition, indicating ranking for limit, and evidence grade filtering for min_grade. This contextualizes the parameters beyond the schema.
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?
Clear verb ('Find'), specific resource ('supplements'), and scope ('for a health condition or goal') with concrete examples. Distinguishes from siblings like 'get_supplements_for_biomarker'.
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?
Implies usage for condition-based queries with examples, but lacks explicit guidance on when not to use or alternatives such as biomarker-based tools. No trade-offs or exclusions stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_top_brandsAInspect
Find the highest-quality brands for a supplement, ranked by ProofMark (Aviado's quality score: third-party testing, certifications, and quality metrics).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (max 20) | |
| supplement | Yes | Supplement name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It explains ranking by ProofMark and criteria, but does not disclose behaviors like result structure when supplement not found, or if read-only (though obvious). Some transparency, but incomplete.
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?
Single sentence, no wasted words. Front-loaded with action and key qualifier. Highly concise and well-structured.
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?
No output schema, but description hints at ranked list of brands. However, lacks details on return format, error handling, or pagination. Completeness is adequate for simple tool but could be improved.
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 covers 100% of parameters with descriptions. Description adds meaning beyond schema by explaining ranking context (ProofMark, quality metrics), improving understanding of 'limit' and 'supplement' despite schema already describing them.
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?
Description clearly states the tool finds highest-quality brands for a supplement, with specific verb 'find' and resource 'brands'. It uniquely identifies the tool among siblings, as no other sibling tool ranks brands.
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?
Implies use when wanting top brands for a supplement, but provides no explicit when-not-to-use or alternatives. No sibling overlaps, so guidance is adequate but minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_conditionsAInspect
Search for a health condition by name with fuzzy matching. Use this to resolve ambiguous condition names before calling get_supplements_for_condition.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., 'sleep', 'anxiety', 'brain fog', 'joint pain') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description bears full burden. It mentions 'fuzzy matching' as a behavioral trait but does not state read-only nature, rate limits, or other safety details. Adequate but not thorough.
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, front-loaded with purpose, no extraneous words. Highly efficient.
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 simple tool with one parameter and no output schema, the description covers purpose, when to use, and parameter. Could mention return format, but not essential for basic use.
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 schema description is clear. The description adds no additional meaning beyond the schema, so baseline 3 applies.
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 uses a specific verb ('Search') and resource ('health condition'), includes 'fuzzy matching', and distinguishes itself from sibling tools like get_supplements_for_condition.
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?
Explicitly states when to use this tool ('resolve ambiguous condition names before calling get_supplements_for_condition'), providing clear guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_supplementsAInspect
Search for a supplement by name with fuzzy matching. Use this to resolve ambiguous names before calling other tools.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., 'fish oil', 'CoQ10', 'NAC') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Mentions 'fuzzy matching' as key behavioral trait, but does not disclose whether tool is read-only, number of results, or sorting. Adequate but lacks details for full transparency.
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: first defines purpose, second gives usage guidance. No words wasted. Information is front-loaded and easy to parse.
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?
Given simple structure (1 param, no output schema), description covers purpose and usage adequately. Missing details about return format or behavior (e.g., list vs single match), but overall sufficient for a search tool with clear sibling differentiation.
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% with a clear description for the only parameter 'query'. Tool description adds 'with fuzzy matching' but this is a behavioral trait, not parameter-level detail. Baseline of 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?
Description uses specific verb 'search' and resource 'supplement by name', adding 'fuzzy matching' for precision. Clearly distinguishes from siblings like get_supplement_info which expects exact names, as indicated by the usage guidance.
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?
Explicitly states 'use this to resolve ambiguous names before calling other tools', providing clear context. Does not list when not to use it, but implicitly suggests alternatives (e.g., get_supplement_info for exact matches) through sibling tool names.
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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