iNutriPlan Supplement Database MCP Server
Server Details
MCP server exposing supplements database used by iNutriPlan.com
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.2/5 across 6 of 6 tools scored.
Most tools have clear, distinct purposes. The only minor overlap is between search_by_ingredient and search_supplements, where both return product results, but the former is specifically for ingredient-based queries and also provides research context.
All tool names use snake_case and follow a consistent verb_noun pattern (e.g., get_*, list_*, search_*). The only deviation is the use of 'list' instead of 'get' for categories, but it remains within the same convention.
With 6 tools, the server is well-scoped for a supplement database. Each tool serves a distinct function (ingredient lookup, product detail, category browsing, searching), and the count is not excessive nor too sparse.
The tool surface covers core operations: ingredient research, product retrieval, category browsing, and flexible search. A minor gap is the absence of a tool to list all known ingredients, which would improve discoverability.
Available Tools
6 toolsget_ingredient_infoAInspect
Look up evidence-graded research context for a supplement ingredient from the curated registry (99 ingredients).
Returns canonical name, aliases, health goals, mechanisms of action, evidence grade (Strong / Moderate / Preliminary), research notes, and recommended iHerb search terms.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Ingredient name, alias, or health goal keyword, e.g. "magnesium", "ashwagandha", "sleep", "testosterone", "weight_loss". |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It covers the read nature implicitly but does not mention behavior for missing ingredients, rate limits, or any side effects. The output schema is present but not shown; the description omits potential constraints or prerequisites.
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 two sentences: the first states the action concisely, and the second lists returned fields. It is front-loaded with a verb ('Look up') and contains no extraneous information, maximizing clarity in minimal space.
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 simple single-parameter tool and the existence of an output schema, the description covers core functionality (what it does and returns). It mentions the registry size (99 ingredients) for context. Minor gaps include lack of error handling details, but overall it is sufficient for a straightforward lookup.
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 provides a detailed description (including examples) for the sole parameter 'name', achieving 100% coverage. The tool description adds that names come from a curated registry of 99 ingredients but does not significantly extend the schema's semantic guidance, resulting in a baseline score.
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: to look up evidence-graded research context for a supplement ingredient from a curated registry of 99 ingredients. It lists specific outputs (canonical name, aliases, health goals, mechanisms, evidence grade, etc.), which distinguishes it from siblings like 'get_supplement_detail' that likely return product-level information.
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 for obtaining curated research context but does not explicitly state when to use this tool versus alternatives (e.g., 'search_by_ingredient' or 'get_supplement_detail'). No when-not-to-use or alternative guidance is provided, leaving the agent to infer context from tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplement_detailAInspect
Retrieve complete details for a single iHerb product by its numeric product ID (visible in iHerb URLs, e.g. "72711" from https://www.iherb.com/pr/p/72711).
Returns the full supplement facts panel (nutrients, amounts, % daily values), ingredients list, warnings, serving info, and the affiliate purchase link.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes | iHerb numeric product ID as a string, e.g. "72711". |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes return content but does not disclose safety (read-only), error handling for invalid IDs, or any side effects. Adequate but could be more explicit.
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 and source, second enumerates return content. No redundancy, front-loaded with essential info. Highly concise and 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?
Given single parameter with full schema coverage, presence of output schema, and clear return description, the description is complete. No missing aspects for an agent to correctly invoke 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 has 100% coverage for product_id with description. The description adds value by explaining where to find the ID (iHerb URLs) and providing an example. Beyond schema, enhances understanding.
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 specific verb 'retrieve' and resource 'complete details for a single iHerb product', with concrete list of returned data. Distinguishes from siblings like get_ingredient_info and search_supplements by focusing on singular product detail retrieval.
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?
Explains when to use: when numeric product ID is available from URL. Does not explicitly mention when not to use or alternatives, but implied by sibling context. Adequate guidance for typical usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_supplements_by_categoryAInspect
List top-rated in-stock products within a specific iHerb category. Use list_categories first to discover valid category slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results (1–30). Default 10. | |
| min_rating | No | Minimum star rating (0.0–5.0). Default 4.0. | |
| category_slug | Yes | Category slug, e.g. "vitamins", "supplements", "brain-cognitive", "sports", "magnesium-complex". |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It mentions 'top-rated' and 'in-stock' filtering but does not detail sorting order, pagination, or error handling for invalid slugs. Additional behavioral context 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, front-loaded with the core purpose. Every sentence adds value: the first states the action and scope, the second provides a critical usage hint. No wasted 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 presence of an output schema (so return values are already documented), three well-described parameters, and no annotations, the description is fairly complete. It covers purpose and a key prerequisite. It could mention what 'top-rated' means (e.g., sorted by rating) or error behavior for missing slugs, but overall it's adequate.
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%, meaning the schema already describes all three parameters with their descriptions and defaults. The description adds minimal extra parameter-level meaning (only the hint to use list_categories for category_slug). With full schema coverage, 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?
Description clearly states 'List top-rated in-stock products within a specific iHerb category', providing a specific verb (List), resource (products), and qualifiers (top-rated, in-stock, specific category). It differentiates from sibling tools like list_categories (which discovers slugs) and search_supplements (likely broader search).
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 'Use list_categories first to discover valid category slugs', establishing a prerequisite and when to use this tool. While it doesn't explicitly list when not to use it or alternatives, the guidance is clear and helpful for correct invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
Return all available iHerb product categories with their URL slugs and live product counts. Use the slug value with get_supplements_by_category to browse products within a specific category.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description fully discloses behavior: returns all categories with slugs and counts. No hidden side effects, read-only nature implied. Minor omission: does not specify if data is live or cached, but overall good.
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, no filler. Front-loaded with action and resource, clear structure.
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?
Output schema covers return values, description adds context about purpose and usage with sibling. Everything an agent needs is present.
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?
No parameters present, so baseline 4 applies. Description adds no parameter info, but schema coverage is 100% (trivially). Effective score is 4 due to zero parameters.
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 explicitly states it returns all iHerb product categories with URL slugs and live product counts. It also mentions the connection to a sibling tool, clearly distinguishing its function.
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?
Directly tells when to use this tool ('to get categories') and when to use a sibling ('use slug with get_supplements_by_category'). Provides clear guidance on next steps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_by_ingredientAInspect
Find products that contain a specific ingredient or compound in their ingredients list (e.g. "ashwagandha", "CoQ10", "zinc bisglycinate").
Also returns evidence-graded research context from the ingredient registry when the ingredient is recognised — goals, mechanisms, evidence grade.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum products to return (1–20). Default 10. | |
| ingredient | Yes | Ingredient or compound name to search for. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It discloses that research context is returned when the ingredient is recognized, which is useful. However, it does not mention any other behavioral traits like rate limits, side effects, or result characteristics beyond that.
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 two paragraphs, front-loaded with the primary action. It is clear and not overly verbose, though could be slightly more concise.
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 that an output schema exists (as per context signals), the description need not explain return format. It covers the main purpose and the extra research context feature. For a search tool with two parameters, it is fairly complete, though it omits details on response structure or error conditions.
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 only examples to the 'ingredient' parameter but no additional semantics beyond what the schema already 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 tool finds products containing a specific ingredient, with examples. It also mentions returning research context, distinguishing it from siblings like get_ingredient_info (likely about the ingredient itself) and search_supplements (probably broader search).
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 when searching products by ingredient but does not explicitly state when to use this tool versus alternatives like search_supplements or get_ingredient_info. No exclusions or when-not-to-use guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_supplementsAInspect
Search the iHerb product database using a natural-language query, benefit keyword, ingredient name, or brand name.
Uses the PostgreSQL GIN full-text search index first (fast, relevance-ranked), then falls back to a broader ILIKE scan if FTS yields no results.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results (1–30). Default 10. | |
| query | Yes | Search term, e.g. "magnesium sleep support", "omega 3 fish oil", "vitamin D immune", or a brand name. | |
| min_rating | No | Minimum star rating filter (0.0–5.0). Default 4.0. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 transparently describes the two-stage search process (GIN FTS then ILIKE fallback) and confirms it's a read-only operation with no destructive behavior.
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-load the purpose, followed by a clear explanation of the algorithm. No wasted words; structure aids quick comprehension.
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?
Description covers purpose, usage, and algorithm. With an output schema present, return value details are unnecessary. Could mention sorting or potential performance of ILIKE fallback, but overall adequate.
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 is 3. The description adds value by explaining how the query parameter is interpreted (e.g., 'magnesium sleep support') and the search algorithm, which enhances parameter understanding.
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 searches the iHerb product database using natural-language queries, benefit keywords, ingredient names, or brand names. It distinguishes from siblings like 'search_by_ingredient' and 'get_supplements_by_category' by covering multiple search types.
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 explains the search algorithm (FTS fallback to ILIKE) but does not explicitly say when to use this tool versus alternatives like 'search_by_ingredient'. It implies general use but lacks direct comparison.
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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