health
Server Details
Supplement research, biomarker effects, drug interactions, and brand quality data
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.1/5 across 8 of 8 tools scored.
Each tool has a clearly distinct purpose: searching, getting supplement info, interactions, biomarker associations, condition associations, and brands. No ambiguity between tools.
All tools follow a consistent verb_noun pattern using snake_case, with 'get_' or 'search_' prefixes, making the intent clear and predictable.
With 8 tools, the server is well-scoped for its domain. Each tool covers a necessary aspect without being overly numerous or sparse.
The tool set covers searching, info retrieval, and relationships between supplements, biomarkers, conditions, interactions, and brands. A minor gap is the lack of a tool to list all available conditions or biomarkers, but search tools likely suffice for discovery.
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 |
|---|---|---|---|
| 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?
Without annotations, the description carries the full burden. It discloses the return fields but does not mention side effects, auth needs, or whether it is read-only. For a lookup tool, this is 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 with zero waste. The purpose is front-loaded and the return fields are listed efficiently.
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?
The tool is simple with one parameter and no output schema. The description explains what it returns, though some details like result limits or pagination are omitted. Adequate for the complexity.
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?
The schema already covers the single parameter with an example. The description adds no extra parameter constraints or semantics beyond what the schema provides.
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 for a given supplement'. It lists the return fields, distinguishing it from sibling tools like get_supplement_info and 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?
The description does not explicitly state when to use this tool over siblings or provide exclusions. Usage is implied by the purpose but lacks direct guidance.
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 carries full burden. It lists categories of returned information but does not disclose any behavioral traits beyond that (e.g., performance, authentication needs, 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?
The description is a single sentence that lists multiple aspects efficiently. While slightly dense, it front-loads the key verb and resource with minimal waste.
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 adequately covers the returned information categories. It mentions safety contraindications, which is additional context. Sibling tools exist, but the scope is clear.
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% (one parameter described as 'Supplement name'), but the description adds no additional meaning beyond what the schema provides. No format, examples, or constraints are given.
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 enumerates specific aspects (identity, function, biomarkers, mechanism, safety). This distinguishes it from siblings like 'get_biomarkers_for_supplement' which likely returns only biomarkers.
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 by listing what info is returned (comprehensive details), but does not explicitly state when to use this tool versus siblings, nor does it provide when-not or alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplement_interactionsAInspect
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?
With no annotations, the description carries full burden. It discloses the tool's read-only nature (no destructive actions) and describes the output (synergies, antagonisms, etc.). It doesn't detail auth needs or pagination, but the behavioral context is sufficient for a read operation.
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 concise sentences, front-loaded with the core purpose. Every word adds value; no fluff.
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 2 parameters, no output schema, and no annotations, the description covers the main functionality and output types. It could mention parameter formatting, but overall complete for typical 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%, so baseline 3. The description reinforces the purpose of the supplement parameter but adds no new meaning beyond schema descriptions. The include_drug_interactions parameter's role is implied by the description's mention of drug-supplement interactions.
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 finds supplement-supplement and drug-supplement interactions, specifying exact numbers of pairs (765 and 1,626) and types (synergies, antagonisms, etc.). It distinguishes from siblings by focusing on interactions rather than general info or biomarkers.
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 interaction data is needed for a supplement, and mentions both supplement and drug interactions. However, it does not explicitly state when to avoid this tool or point to alternatives like get_supplement_info for general details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplements_for_biomarkerAInspect
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 |
|---|---|---|---|
| biomarker | Yes | Biomarker name, abbreviation, or LOINC code |
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 aspects. It states returns are evidence-weighted from 4,794 edges, hinting at data provenance. However, it does not clarify if the operation is read-only, any required permissions, or potential side effects. The description adds some context but is not fully transparent.
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 extremely concise with two sentences. The first sentence immediately states the core purpose with an example, and the second adds important output detail. 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 the tool's simplicity (1 parameter, no output schema, no annotations), the description covers the essential purpose and hints at output quality. However, it lacks explanation of evidence weighting, potential empty results, or how to interpret the output. Adequate but with room for improvement.
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?
The input schema already describes the single parameter (biomarker) with 100% coverage (name, abbreviation, or LOINC code). The tool description adds no additional semantic value for the parameter, only elaborating on output. Baseline of 3 is appropriate given full schema coverage.
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 function: 'Find which supplements affect a given biomarker'. It provides specific examples (triglycerides, CRP, vitamin D) and distinguishes itself from sibling tools like get_biomarkers_for_supplement by its direction of relation.
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 when to use the tool: to discover supplements that impact a specified biomarker. It mentions evidence-weighted results, giving confidence. However, it does not explicitly state when not to use it or mention alternatives, though sibling names provide context.
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?
No annotations provided, so description must disclose behavior. It states returns evidence-graded recommendations with dosages, which is a key behavioral detail. It does not cover limitations or edge cases but is adequate for a read-only lookup tool.
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 redundant information. Every word earns its place.
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 3 parameters and no output schema, the description covers purpose and return content. It could mention behavior when condition is not found or pagination, but is largely complete for a simple tool.
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%, so baseline is 3. The description adds context about output (evidence grades, dosages) but does not enhance parameter understanding 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?
The description clearly states the tool finds supplements for a health condition or goal, with examples. It distinguishes from siblings like search_supplements (general) and get_supplements_for_biomarker (specific to biomarkers).
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 provides clear context for when to use (health conditions/goals) but does not explicitly contrast with alternatives like search_conditions or get_supplement_info. It lacks explicit when-not guidance but implied use case is clear.
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 AviScore (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 are provided, so the description bears full responsibility. It reveals the ranking method (AviScore) but does not disclose whether the tool is read-only, how errors are handled (e.g., invalid supplement), or any rate limits. Minimal behavioral insight beyond the core purpose.
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 sentence that directly states the tool's function with no extraneous words. It is front-loaded with the purpose and efficiently conveys key details.
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 two parameters and no output schema, the description covers the main purpose and ranking logic. It lacks handling of edge cases (e.g., supplement not found) or additional behavior, but overall it is sufficient for an agent to understand basic usage.
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?
The input schema has 100% coverage with descriptions for both parameters ('supplement' and 'limit'). The description adds value by explaining that results are ranked by AviScore, which clarifies the ordering beyond what the schema states.
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 action (Find), the resource (highest-quality brands for a supplement), and the ranking criterion (AviScore). It distinguishes from sibling tools that target supplements or biomarkers, making the purpose unambiguous.
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 implicitly indicates usage when a supplement name is provided and the user wants top brands, but it does not explicitly state when to prefer this tool over alternatives or mention any exclusions (e.g., no valid supplement). Lacks explicit when-to-use guidance.
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?
With no annotations provided, the description must fully disclose behavioral traits. It mentions fuzzy matching but does not specify return format, number of results, or case sensitivity. For a search tool, additional context about result limits or ranking would improve 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 concise sentences that front-load the purpose and immediately provide usage guidance. Every sentence adds value with no waste.
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 search tool with one parameter and no output schema, the description is sufficient. It covers purpose, behavior (fuzzy matching), and usage context. A small improvement would be to mention that results are condition names, but overall it's complete.
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 description adds the key behavioral detail of 'fuzzy matching' beyond the schema's parameter description. This adds meaning beyond what the schema provides, justifying a score above baseline 3.
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 the specific verb 'Search' and resource 'health condition by name', and mentions fuzzy matching. It clearly distinguishes from the sibling tool 'get_supplements_for_condition' by stating that this tool is to be used before that one.
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 condition names before calling get_supplements_for_condition.' This tells the agent exactly when to use this tool and the alternative follow-up, providing strong usage guidance.
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?
Discloses fuzzy matching behavior, which is key. No annotations provided, so description carries full burden. It doesn't mention return format or limitations, but the tool is simple and the context (one string param) makes it adequate.
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 action and purpose. Every word earns its place; no fluff.
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 search tool with one parameter, no output schema, and clear sibling context, the description is complete. It explains what it does, when to use it, and how it relates to other tools.
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 parameter descriptions (query with examples). The description adds no additional parameter semantics beyond the overall purpose; 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?
Clearly states it searches supplements by name with fuzzy matching, and explicitly distinguishes from sibling tools (get_*) by noting it resolves ambiguous names before calling others.
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 advises when to use: 'Use this to resolve ambiguous names before calling other tools.' Implies not for getting detailed info, which is handled by siblings.
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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