Neutral, payment-blind reference of 1,682 dermacosmetic products: lookup, search, alternatives.
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Average 3.9/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: find_alternatives discovers same-category products, list_brands lists brands with counts, lookup_product returns detailed facts for a specific product, and search_products allows 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 the set predictable and easy to navigate.
With 4 tools, the set is appropriately scoped for a neutral reference server, covering the core operations: search, listing, lookup, and alternatives. It is on the smaller side but fits the limited domain.
The tool surface covers key use cases for a neutral product reference: search, detailed lookup, brand listing, and alternative discovery. A minor gap is the lack of a tool to list all products or categories directly, though search can compensate.
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?
With no annotations provided, the description carries the full burden. It discloses that results are alphabetical and never ranked, and frames it as discovery. However, it does not mention authentication, rate limits, or behavior on missing products.
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 with no filler. It front-loads the core purpose and then adds important behavioral context, making every sentence earn 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?
For a single-parameter tool with no output schema, the description is mostly complete. It explains the method (alphabetical), scope (same category), and what it is not. It could mention case sensitivity or behavior when the product is not found, but overall it is sufficient.
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 description need not add much. The description does not elaborate on the query parameter beyond the schema's description, so it meets the baseline.
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 returns other products in the same category, sorted alphabetically. It explicitly distinguishes itself from recommendation tools, making its 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 indicates this is for neutral discovery, not recommendations, and mentions that personalized alternatives are elsewhere. While it does not explicitly state when not to use it, the context and sibling tool names help guide appropriate use.
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?
Without annotations, the description carries full burden. It discloses that the tool returns a list of brands along with product counts, implying a read-only operation. No side effects or auth needs are mentioned, but for a simple listing tool 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?
The description is a single concise sentence of 14 words that immediately states the core function. No wasted words, and it is properly front-loaded with the action and resource.
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 has no parameters and no output schema, the description sufficiently conveys what the tool returns (brands with product counts). It lacks mention of ordering or empty results, but for a simple list tool, the description is complete enough for agent 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?
The input schema has no parameters, so schema description coverage is 100%. The description does not add parameter semantics because none exist, meeting the baseline of 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 clearly states the tool lists dermacosmetic brands covered in MHS BLOOM's reference, including a count of products per brand. This verb+resource specification distinguishes it from sibling tools like find_alternatives, lookup_product, and search_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 listing brands with product counts, but does not explicitly provide when-to-use or when-not-to-use guidance, nor mentions alternatives. This is implied usage, not explicit guidance.
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?
No annotations are provided, so the description carries full burden. It mentions returning descriptive facts and a link, but fails to disclose any behavioral traits such as data sources, latency, or authorization requirements. It does not state if the tool is read-only or if it makes external API calls.
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 concise sentences that front-load the main purpose and output, with no unnecessary words. It efficiently conveys what the tool does and its limitation.
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 simplicity (one parameter, no output schema), the description covers the core functionality and output elements. However, it lacks details about the output structure or pagination, but this is partially compensated by the sibling context and the tool's clear scope.
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 example in the parameter description. The tool description adds overall context but does not significantly enhance the meaning of the query parameter 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 identifies a specific skincare product and returns detailed factual information, including brand, ingredients, concerns, etc. It distinguishes itself from siblings like search_products (which likely returns multiple results) and find_alternatives (which suggests alternatives) by emphasizing 'neutral reference' and 'descriptive only'.
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 gives context that the tool is for descriptive facts only and not personal skin verdicts, but does not explicitly state when to use it versus sibling tools like list_brands or search_products. The guidance is implied but not direct.
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 provided, the description carries the full burden. It discloses key behavioral traits: the output is neutral, alphabetical, includes reference links, and never ranked or rated. This is good, but it does not mention authentication needs, rate limits, or that it is a read-only operation. The description adds value beyond the empty annotations.
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 without wasted words. It front-loads the core purpose and output characteristics. However, it could be slightly more structured (e.g., bullet points) for easier scanning. Still, it is appropriately sized.
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 tool with 6 parameters and no output schema, the description covers the basic behavior but lacks details on pagination, default limits, result handling (e.g., empty results), error states, or how parameters interact. It does not reference sibling tools or provide selection guidance. Adequate but not 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 only 17% (only limit has a description). The description paraphrases the parameter names (ingredient, concern, category, brand, fragrance_free) but adds little extra meaning beyond the schema field names. It does not provide examples, format constraints, or detailed semantics. The description partially compensates for low coverage but not sufficiently.
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 MHS BLOOM's product reference by multiple criteria (ingredient, concern, category, brand, fragrance_free) and returns a neutral alphabetical list with reference links. It distinctively notes the output is never ranked or rated, differentiating it from sibling tools like find_alternatives or list_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?
The description implies usage when searching by specific criteria but does not explicitly tell when to use this tool versus alternatives like find_alternatives or lookup_product. There is no guidance on when not to use it or what scenarios are unsuitable.
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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