Query an independent, payment-blind reference of 1,682 dermacosmetic products — by product, ingredient, concern, category or brand. Neutral descriptive facts, every result links back to its reference page. No ratings, no paid placement.
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Average 4/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: find_alternatives for same-category products, list_brands for brand overview, lookup_product for detailed product info, and search_products for multi-criteria search. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (find_alternatives, list_brands, lookup_product, search_products), making them predictable and easy to understand.
Four tools is perfectly scoped for a neutral skincare reference: search, lookup, brand listing, and alternatives. Each tool earns its place without being too few or too many.
The tool set covers the core needs of a reference catalog: searching, retrieving details, listing brands, and finding alternatives. There are no obvious gaps given the stated purpose of neutral discovery.
find_alternativesFind neutral alternatives in the same categoryAInspectGiven a product, return other products in the SAME category from the catalog, listed neutrally (alphabetical, never ranked). This is discovery, not a recommendation — the personal best-fit and price-tiered alternatives live in the app.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The product to find alternatives for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that results are listed neutrally (alphabetical, never ranked), which is a key behavioral trait. However, with no annotations provided, it fails to mention edge cases like what happens if the product is not found or has no alternatives, reducing 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?
The description is extremely concise—two sentences with no fluff. It front-loads the core action and adds relevant constraints, making it easy for an agent to parse 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 (1 param, no output schema, no annotations), the description covers the main purpose and behavior adequately. It lacks only details about error conditions or empty results, which would raise completeness to 5.
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 'query' with a description. The tool description adds mild context ('Given a product') but does not significantly extend meaning beyond the schema, 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: given a product, return other products in the same category from the catalog, listed neutrally (alphabetical). This distinguishes it from sibling tools like lookup_product or search_products, which serve different use cases.
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 defines when to use the tool (discovery) and when not to (recommendations, which are handled by the app). It provides clear context but stops short of naming specific alternative tools for recommendation, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_brandsList the brands coveredAInspectList the dermacosmetic brands covered in MHS BLOOM's reference, with how many products each has.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses that the output includes brand names and product counts, which is sufficient for a read-only list tool. No contradictions.
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 that front-loads the purpose and includes key output detail. No 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 no output schema and simple functionality, the description adequately conveys what the tool returns. Could optionally mention ordering or format, but not necessary.
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 in schema, so baseline is 4. Description does not need to add param details as 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?
Description clearly specifies the tool lists dermacosmetic brands and product counts, with a clear verb and resource. It is easily distinguished from sibling tools like 'find_alternatives' and 'search_products' which serve different purposes.
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 when-to-use or when-not-to-use guidance is provided. However, the purpose is clear and sibling names imply differentiation, so an agent can infer usage context, but explicit guidelines are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_productLook up a skincare productAInspectIdentify a dermacosmetic product in MHS BLOOM's neutral reference and return its descriptive facts (brand, key ingredients, the concerns it's oriented toward, texture, fragrance, price band, who it's aimed at) plus a link to its reference page. Descriptive only — not a personal skin verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Product name and/or brand, e.g. "CeraVe foaming cleanser". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates the tool is read-only and factual via 'neutral reference' and 'descriptive only'. However, it omits details such as data freshness, authentication requirements, rate limits, or potential error cases. The description is adequate for a simple lookup but 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, consisting of two clear sentences. The first sentence front-loads the purpose, resource, and output; the second clarifies scope. No redundant or unnecessary words. 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?
Given the tool's simplicity (one parameter, no output schema, no nested objects), the description adequately explains what the tool does and what it returns (descriptive facts list and link). It mentions the specific fields returned. Minor omissions: no mention of how to handle multiple matches, exact matching rules, or error cases. Otherwise, it is 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?
The input schema has 100% coverage with a clear description for the 'query' parameter. The tool description adds no new parameter-level guidance beyond the schema, but it lists the output fields (brand, ingredients, etc.), which indirectly clarifies the query should be a product name. Per the baseline for high schema coverage, a score 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?
The description clearly states the verb 'Identify' and the specific resource 'a dermacosmetic product in MHS BLOOM's neutral reference'. It lists the returned descriptive facts (brand, ingredients, concerns, etc.) and a link, effectively distinguishing from siblings like find_alternatives, list_brands, and search_products. The last sentence clarifies it is descriptive only, not a personal skin verdict, adding differentiation.
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 use: it is a neutral reference lookup for factual product information. It explicitly states 'Descriptive only — not a personal skin verdict,' guiding the agent not to use this for recommendations. However, it does not directly compare with sibling tools or state when not to use it (e.g., for alternatives or brand listings), so it misses explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsSearch products by ingredient / concern / category / brandAInspectSearch MHS BLOOM's reference for products by any of: key ingredient (e.g. "niacinamide"), skin concern (e.g. "blemishes"), category (e.g. "sunscreen"), brand, and/or fragrance_free. Returns a neutral, alphabetical list with reference links. Never ranked or rated.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | ||
| limit | No | Max results (default 15, max 40). | |
| concern | No | ||
| category | No | ||
| ingredient | No | ||
| fragrance_free | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full weight for behavioral disclosure. It specifies return format (alphabetical list with reference links), neutrality (never ranked or rated), and the source. It does not cover auth or rate limits, but for a read-only search 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, no unnecessary words. Critical info (parameters, behavior, constraints) is front-loaded. 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 6 parameters, no output schema, and no annotations, the description covers the tool's purpose, parameters, and behavioral traits. It lacks default/max limit details (present in schema) and does not explain multi-parameter interaction, but is largely sufficient for an agent to understand and use the 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 coverage is low (17% only for limit). Description lists the five search parameters with examples (e.g., 'niacinamide' for ingredient). This adds meaning beyond bare types, but does not detail format or interaction logic (AND/OR). Partial compensation for low 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?
Description clearly states the tool searches for products by multiple criteria (ingredient, concern, category, brand, fragrance_free). It includes a specific source ('MHS BLOOM's reference') and examples, making the purpose distinct. However, it does not explicitly differentiate from sibling tools, though the purpose is implied.
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 product searches but provides no explicit guidance on when to use it versus siblings (e.g., find_alternatives, list_brands, lookup_product). No exclusions or when-not-to-use scenarios are mentioned.
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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