iNutriPlan Supplement Database MCP Server

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

Discloses return fields and notes the registry size (99 ingredients). With no annotations, it provides moderate transparency about what the tool does, but lacks information on error handling (e.g., unknown ingredient) or any 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 cover action, source, and return fields efficiently. Front-loaded with the primary purpose and no redundant information. Every sentence 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?

Well-suited for a simple lookup tool with one parameter and an output schema. Lists returned fields and registry scope. Could be improved by noting possible errors (e.g., ingredient not found), but overall sufficiently 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 description coverage is 100% and already explains the parameter well (name, alias, or health goal keyword). The tool description reinforces the purpose but adds little beyond what the schema provides, so baseline score 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 looks up evidence-graded research context for supplement ingredients from a curated registry. Lists return fields. However, it does not explicitly differentiate from sibling tools like get_supplement_detail or search_by_ingredient, which could cause ambiguity.

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 on when to use this tool versus alternatives. The description implies use for ingredients, but there is no mention of when not to use it or which sibling tool to choose for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description discloses the return content (supplement facts, ingredients, warnings, serving info, affiliate link) but does not discuss authentication, rate limits, or side effects. It is sufficiently transparent for a read-only retrieval 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?

The description is two focused sentences, front-loaded with the action, providing essential information without any 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?

Given the low complexity (1 required parameter, output schema present), the description fully explains purpose, parameter, and return content, leaving no 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%, and the description adds value by explaining the parameter format (numeric string from URL) with an example, enhancing clarity 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 'Retrieve complete details for a single iHerb product by its numeric product ID,' specifying the verb and resource. It distinguishes from sibling tools like search_supplements or list_categories by focusing on retrieving details for a specific product.

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 you have a numeric product ID and need full details, but does not explicitly state when to use this tool over alternatives or provide when-not scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It states the tool returns top-rated and in-stock products, implying filtering by rating and availability. This is adequate for a straightforward read operation; no side effects or destructive actions are indicated.

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 only two sentences, with the first clearly stating the purpose and the second providing essential prerequisite guidance. Every sentence adds value, no fluff 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 presence of an output schema (so return values need not be explained) and three well-documented parameters, the description is adequate. It covers purpose, prerequisite, and basic behavior. It could mention sorting order (e.g., by rating descending) but is otherwise complete for a simple listing 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?

The input schema has 100% description coverage, so the baseline is 3. The description adds the context of 'top-rated' and 'in-stock' but does not explain parameter syntax or usage beyond what the schema already provides (e.g., defaults and ranges).

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 lists top-rated in-stock products within a specific iHerb category, providing a specific verb (list), resource (products), and scope (category). This distinguishes it from sibling tools like get_supplement_detail (single product) and list_categories (lists categories).

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 explicitly instructs to use list_categories first to discover valid category slugs, which is excellent guidance for the agent. It implies when to use this tool (when you have a category and want top-rated in-stock items) but does not explicitly state when not to use or mention alternatives like search_supplements.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the burden. It discloses that the tool is a read operation returning data (categories, slugs, counts). It doesn't add further behavioral details like performance or size, but for a simple list with no parameters, this is 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 purpose, then usage guidance. Every word is necessary and no redundancy.

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 parameters, an output schema exists (from context), and the description explains what is returned and how to use the result. The description is fully complete for this 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?

The tool has 0 parameters, so baseline is 4. The description correctly provides no parameter info since none exist.

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 'Return all available iHerb product categories with their URL slugs and live product counts', specifying the verb 'Return', resource 'categories', and exact data returned. It also distinguishes itself from siblings by mentioning the slug is used with get_supplements_by_category.

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 explicitly says 'Use the slug value with get_supplements_by_category to browse products within a specific category', providing a clear use case. While it doesn't mention when not to use it, the context is sufficient for an agent to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the transparency burden. It discloses two key behaviors: returns products and includes evidence-graded research context when ingredient is recognized. This goes beyond basic listing. However, it does not mention error handling or behavior for unrecognized ingredients.

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, no fluff. Main purpose is stated first, then additional detail about research context. Every sentence adds value.

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 an output schema exists (not shown), the description partially covers return values by mentioning research context. It could mention whether products are paginated or what happens with unrecognized ingredients, but examples and scope are 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% with both 'ingredient' and 'limit' parameters described in the schema. The description does not add extra semantic context beyond what the schema already provides, so 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 tool's purpose: to find products by ingredient and also return research context. It distinguishes from siblings like 'get_ingredient_info' (which focuses on ingredient details) and 'search_supplements' (broader search). Uses specific verb 'Find' and resource 'products'.

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 ingredient-based product search but does not explicitly state when to use this vs alternatives like 'get_ingredient_info' or 'search_supplements'. No when-not guidance provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Discloses the dual search strategy: FTS index first, then ILIKE fallback. With no annotations, this provides useful behavioral context. Does not mention auth or rate limits, but adequate for a read-only search.

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 short paragraphs, no redundant text. The first sentence immediately states the purpose. Technical details are in a separate paragraph. Every sentence contributes.

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?

With an output schema present, the description need not detail return values. It covers the main behavioral aspect (search strategy). Could mention ordering or further filtering, but overall sufficient for 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 already covers all 3 parameters (100% coverage). The description adds value by explaining how the query parameter uses FTS and ILIKE. This goes beyond the schema's brief description, 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?

Clearly states the tool searches a product database using natural-language query, benefit keyword, ingredient, or brand name. It differentiates from siblings like search_by_ingredient and get_supplements_by_category.

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?

Does not explicitly state when to use this tool vs alternatives like get_ingredient_info or search_by_ingredient. The description implies a general search function but lacks usage context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.