MCP server for makeup.land — an Israeli professional cosmetics retailer. Catalog tools (list_products with cross-lingual semantic search and ΔE-ranked shade matching that returns shade_match: {hex, delta_e} per product, plus validate_gift_card) work anonymously. Customer-data tools (list_brands, get_customer, get_cart, list_orders, list_payment_links, get_customer_best_deals) need a bearer token issued via shop@makeup.land.
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Average 3.9/5 across 8 of 8 tools scored.
Each tool targets a distinct entity or action: cart, customer, deals, brands, orders, payment links, products, and gift cards. There is no overlap or ambiguity between tool purposes.
Tool names follow a consistent verb_noun pattern: get_ for single resources, list_ for collections, and validate_ for a specific action. The one outlier, get_customer_best_deals, still fits the get_ prefix pattern, so it's mostly consistent.
With 8 tools, the set is well-scoped for a customer/catalog read API. Each tool serves a clear purpose and none are redundant.
The surface covers customer info, cart, orders, payments, products, brands, deals, and gift cards, which is comprehensive for a read-only domain. Minor gaps like lacking order details or product-level details are workable, and mutations are clearly out of scope.
get_cartFetch a customer's cartAInspectReturn the customer's most-recently-updated cart with per-line and total reward projection. Bearer + phone required.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | E.164 phone selecting the customer whose cart to return. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It specifies auth requirements, selection behavior (most-recently-updated), and return contents (reward projection). However, it does not explicitly state read-only behavior or how missing carts are handled, leaving some ambiguity.
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 that front-load the primary function, then add auth and return details. There is no unnecessary content, making it appropriately concise for a simple tool.
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 one-parameter tool with no output schema, the description adequately covers the return value and auth requirement. It could mention empty-cart behavior or error cases, but the core functionality is clearly specified.
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 the phone parameter fully described, so the description adds minimal extra meaning beyond mentioning 'Bearer + phone required'. This aligns with the baseline of 3 when the schema handles parameter documentation.
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 states it returns the customer's most-recently-updated cart with per-line and total reward projection, using a specific verb and resource. This clearly distinguishes it from sibling tools like get_customer and list_orders.
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 is provided on when to use this tool versus alternatives. The mention of 'Bearer + phone required' is a prerequisite rather than usage context. The description does not reference any sibling tools or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_customerLookup customer by phoneAInspectReturn tags, ℳ-credit balance, M Club tier for the customer with the given E.164 phone. Bearer required.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | E.164 phone number, e.g. +972501234567. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the auth requirement ('Bearer required') and the return fields, but doesn't explicitly state read-only behavior, error handling, or other side effects. This is partially transparent but leaves some ambiguity.
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 main action and returning the specific data fields. Every word adds value, and the structure is immediately scannable.
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 single-parameter lookup with no output schema, the description adequately covers what the tool returns and the auth requirement. It doesn't describe response format or errors, but the tool's complexity is low, making this 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 description coverage is 100%, so the baseline is 3. The description repeats the E.164 requirement but adds no new information about the phone 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?
Description explicitly states it returns specific customer attributes (tags, ℳ-credit balance, M Club tier) for a customer identified by phone. This clearly distinguishes it from sibling tools like get_customer_best_deals and list_orders.
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 the tool: when you need customer profile details by phone number. It implies the use case without explicitly listing alternatives or exclusions, making it a clear but not fully explicit guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_customer_best_dealsPersonalised best deals for a customerAInspectTop deal projections based on the customer's tags + M Club tier. Bearer + phone required.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max deals to return. | |
| phone | Yes | E.164 phone selecting the customer. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral disclosure burden. It reveals the selection logic (tags, M Club tier) and that Bearer auth is required, which is useful. However, it does not state explicitly whether the operation is read-only, what the response format is, or any side effects, leaving gaps.
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, front-loaded with the core purpose, and the second sentence states necessary credentials. Every word earns its place with 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?
For a simple tool with 2 params and a clear schema, the description is mostly adequate. However, since there is no output schema, it should clarify what 'top deal projections' returns (e.g., an array of deals) and any default behavior. This is a moderate gap.
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 provides descriptions for both parameters (phone, limit), giving 100% coverage. The description adds no additional parameter-specific meaning beyond what the schema offers, so the baseline 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 gives a specific verb phrase 'Top deal projections' and identifies the resource (customer's tags + M Club tier). This clearly distinguishes it from sibling tools like list_products or get_customer, 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?
The description implies usage for retrieving personalized deals based on customer attributes, but provides no explicit when-to-use vs alternatives or exclusions. It mentions 'Bearer + phone required' which is a prerequisite, not a usage guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_brandsList all brandsAInspectReturn every brand with product counts and slugs. Bearer required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosure. It adds value by stating the required Bearer authentication and the specific output fields (product counts and slugs). However, it does not mention potential pitfalls like pagination, rate limits, or exact response structure, which would be useful for a production consumer.
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, front-loaded sentence stating exactly what the tool does, followed by a crucial authentication requirement. There is no wasted verbiage; 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?
For a simple list tool with no parameters, the description is complete: it states the resource, the included fields, and the auth requirement. Although there is no output schema, the description's mention of 'product counts and slugs' sufficiently conveys the expected return structure.
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 zero parameters, so the baseline is 4. The description adds no parameter-level information, but none is needed since the input schema is empty. The schema coverage is effectively 100%, and the description is not expected to elaborate on nonexistent 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?
The description uses the specific verb 'Return' with the resource 'brands' and specifies the output includes 'product counts and slugs.' This clearly distinguishes it from sibling tools like list_products or list_orders, which operate on different entities.
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 clearly indicates this tool is for retrieving all brands, and the context of 'every brand' implies use when a complete list is needed. While it doesn't explicitly exclude alternatives, the sibling tools are all for different resources, making the usage context unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_ordersList a customer's ordersAInspectRecent orders with 6-axis status (order / payment / fulfillment / delivery / return / review). Bearer + phone required.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-indexed page number. | |
| limit | No | Page size. | |
| phone | Yes | E.164 phone selecting the customer. |
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 does add useful context: authentication requirement (Bearer + phone) and the 6-axis status. But it does not explicitly state that this is a read-only operation, nor does it explain pagination behavior or what 'recent' means, leaving some ambiguity.
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, compact sentence that conveys the core purpose and a key requirement. It is front-loaded with the most important information, contains no redundant words, and is easily scannable.
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 list tool with full schema coverage, the description is fairly complete: it names the resource, output axes, and the required auth. Minor gaps include not specifying pagination defaults or the timeframe for 'recent', but these are not critical given 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?
The schema already documents all three parameters with meaningful descriptions (page, limit, phone), and marks phone as required. The description emphasizes that phone is required, which is already in the schema, but adds no additional parameter-level semantics beyond that.
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 title and description clearly state that the tool lists a customer's recent orders, with a specific 6-axis status breakdown (order/payment/fulfillment/delivery/return/review). The scope is unambiguous (customer's orders) and it is distinct from sibling tools like list_products or list_payment_links.
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 the tool is used for retrieving recent orders with status details and states the required auth method (Bearer) and required parameter (phone). However, it does not explicitly explain when to use this tool versus alternatives, nor does it mention any exclusions or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_payment_linksList pending payment linksAInspectPending payment_requests on the customer's unpaid orders. Bearer + phone required.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | E.164 phone selecting the customer. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the scope (unpaid orders) and authentication requirement (Bearer + phone), which is useful. However, it doesn't explicitly state that this is a read-only operation or describe any side effects, rate limits, or error conditions. The title 'List' implies read-only, but the description doesn't add much beyond that and the scope.
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 immediately state the core function and requirements. No filler or redundancy. It is front-loaded and every word contributes 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?
For a simple tool with one parameter and no output schema, the description adequately explains what it returns (pending payment_requests) and the required context (unpaid orders, auth). It falls slightly short of a 5 because it doesn't provide any hint about the response structure or optional parameters, but overall it is sufficient for an agent to select and invoke the tool correctly.
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 covers the lone parameter 'phone' with a clear description and pattern. The tool description repeats that phone is required but adds no new semantic information beyond the schema. Since schema coverage is 100%, the baseline of 3 applies.
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 pending payment requests associated with the customer's unpaid orders. It uses a specific verb ('list') and resource ('pending payment links'), and distinguishes itself from sibling tools like list_orders or get_cart 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?
The description provides clear context: this is for pending payment_requests on the customer's unpaid orders, and it explicitly requires Bearer + phone. While it doesn't name alternative tools or exclusion scenarios, the context is specific enough for an agent to know when to invoke this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_productsList / search productsAInspectBrowse the catalog with tag, brand, near_hex (ΔE shade match), hue_family, and sort filters. Bearer becomes required when passing phone (rewards projection).
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Natural-language query. Cross-lingual semantic search — `lipstick`, `שפתון`, `lápiz labial` each return Hebrew-tagged lipsticks (the sets may differ across languages but are semantically equivalent). Prefer this over `tag` for any category lookup that isn't already in exact Hebrew. | |
| tag | No | Filter by an EXACT tag string. Tags are Hebrew (e.g. שפתון, ביוטי, עיניים, שפתיים). English category names rarely match — use `q` instead for natural-language queries. | |
| page | No | 1-indexed page number. | |
| sort | No | Sort order. Default: relevance. | |
| brand | No | Filter by brand slug or name. | |
| limit | No | Page size, default 20. | |
| phone | No | E.164 phone for per-customer rewards projection. REQUIRES bearer auth. Omit for anonymous catalog browse. | |
| near_hex | No | Target hex color for ΔE-ranked shade matching. Examples: '#C2185B', 'E8D4B8'. Each returned product carries a `shade_match: {hex, delta_e}` field with the closest variant swatch and its perceptual distance. | |
| hue_family | No | Post-filter on hue family. Must be paired with another filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose an important behavioral trait: Bearer auth becomes required when passing 'phone'. However, it does not state return format, pagination behavior, or explicitly mark the tool as read-only. The schema descriptions add some behavioral details (e.g., shade_match returned with near_hex), but the main description remains thin on overall 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?
The description is two sentences long and front-loads the core purpose ('Browse the catalog'), then adds the auth dependency. Every word earns its place; it avoids redundancy with schema 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?
Given the tool's complexity (9 parameters, no output schema, no annotations), the overall definition is fairly complete thanks to the rich schema descriptions. The main description gives a good overview, but it lacks an explicit statement of what the tool returns (e.g., a list of products with pagination). Still, the tool's purpose is clear and the schema fills in most 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 description coverage is 100%, so the baseline is 3. The main description adds no new parameter meaning; it merely mentions 'near_hex' and 'hue_family', which are already documented in the schema. The schema itself already provides rich, contextual descriptions for each parameter.
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 'Browse the catalog' and the title is 'List / search products', making the tool's purpose specific and unambiguous. It distinguishes from sibling tools like list_brands and list_orders by focusing on product catalog operations.
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 context that this tool is for catalog browsing/searching, but it does not explicitly mention when to use it over alternatives like list_brands or get_customer. The advice to prefer 'q' over 'tag' is inside the parameter descriptions, not the main tool description, so guidance on tool selection is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_gift_cardValidate a gift card codeAInspectCheck a gift card's remaining balance using its code. Public endpoint — no bearer required (gated on knowledge of the code).
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Gift card code (full string as printed on the card). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It reveals that the endpoint is public and gated on code knowledge, which is a useful auth-related trait. It does not mention error cases or rate limits, but for a simple balance check, the key behavioral trait (no bearer required) is disclosed.
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 primary purpose and then provide key auth context. Every word earns its place, and there is no fluff or 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?
For a simple one-parameter tool with no output schema, the description is complete. It explains what it does, the auth requirement, and implies the return value (remaining balance). The absence of an output schema is mitigated by the explicit statement of what is checked.
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 provides a 100% coverage description for the 'code' parameter ('Gift card code (full string as printed on the card).'). The tool description adds marginal value by mentioning 'using its code,' but this largely restates the schema. 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 function: 'Check a gift card's remaining balance using its code.' It uses a specific verb+resource (check balance, gift card, code) and distinguishes itself from sibling tools like get_cart or list_products, none of which deal with gift cards.
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: it is a public endpoint and no bearer token is required, gated on knowledge of the code. It does not explicitly mention alternatives or when-not to use it, but given the absence of related sibling tools, this is a minor gap and context is sufficient for an agent to select it appropriately.
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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{
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