Sephora MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: search_products finds items, get_product provides details, add_to_basket adds items, view_basket shows contents, checkout completes purchase, and get_rewards handles loyalty program. The descriptions explicitly guide usage flow, eliminating any ambiguity.
Naming Consistency5/5
All tools follow a consistent 'sephora_verb_noun' pattern with snake_case throughout. The verbs (add_to, checkout, get, search, view) are descriptive and uniformly applied, making the set predictable and easy to navigate.
Tool Count5/5
Six tools are well-scoped for an e-commerce server, covering the core shopping workflow from discovery to purchase and account management. Each tool earns its place without redundancy, and the count is manageable for agents to understand and use effectively.
Completeness5/5
The toolset provides complete coverage for Sephora's shopping domain: product discovery (search_products), detail retrieval (get_product), basket management (add_to_basket, view_basket), checkout (checkout), and account features (get_rewards). No obvious gaps exist, supporting end-to-end agent workflows.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'view' operation which implies read-only behavior, but doesn't explicitly confirm this or mention other behavioral traits like authentication requirements, rate limits, error conditions, or whether it requires an active session. The description provides basic functional information but lacks important behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose and scope. It's front-loaded with the main action ('View the current contents') and then specifies what information is included. Every word earns its place with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with no parameters and no output schema, the description provides adequate functional information about what the tool returns. However, it lacks important context about authentication requirements, session dependencies, or error handling that would be valuable for an agent. The absence of annotations means the description should compensate more for behavioral transparency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents the empty parameter set. No additional parameter semantics are needed or provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'View the current contents of the Sephora shopping basket' with specific details about what information is included (items, quantities, prices, subtotal, estimated totals). It uses a specific verb ('view') and resource ('Sephora shopping basket'), but doesn't explicitly differentiate from sibling tools like 'sephora_checkout' which might also show basket contents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool should be used instead of other basket-related operations or what prerequisites might be needed (like having items in the basket). No explicit when/when-not statements or alternative tool references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only covers what data is returned, not behavioral aspects like rate limits, authentication needs, error conditions, or pagination for reviews. It mentions reviews can be included/limited but doesn't describe review format or sorting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence efficiently lists all key information elements without waste. Front-loaded with core purpose, followed by specific data components. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with 3 parameters (100% schema coverage) but no annotations or output schema, the description adequately covers what data is returned but lacks behavioral context (rate limits, errors) and output format details. Minimum viable but with clear gaps in transparency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no additional parameter semantics beyond implying product_url identifies 'a specific Sephora product' and that reviews are part of the output. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'detailed information about a specific Sephora product', listing specific data elements (description, ingredients, instructions, variants, reviews). It distinguishes from siblings like sephora_search_products (search vs. specific product) and sephora_add_to_basket (retrieval vs. action).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving detailed product data, but doesn't explicitly state when to use this vs. alternatives like sephora_search_products (for browsing) or sephora_get_rewards (for loyalty info). No explicit exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 clearly indicates this is a write/mutation operation ('places the order') and mentions the testing capability via 'dry_run'. However, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error conditions, or what happens when promo codes are invalid. The description adds some value but leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the purpose and key actions, while the second provides crucial behavioral context about testing. There's zero wasted text, and the most important information (the tool's purpose) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex mutation tool with 15 parameters and no annotations or output schema, the description is adequate but incomplete. It covers the basic purpose and testing capability well, but doesn't address important contextual aspects like authentication requirements, error handling, or what the tool returns. Given the complexity and lack of structured metadata, a more comprehensive description would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 15 parameters thoroughly. The description mentions 'promo codes' and 'dry_run' specifically, but doesn't add meaningful semantic context beyond what's in the schema descriptions. The baseline of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Complete a Sephora purchase') and enumerates the key steps involved (fills shipping address, applies promo codes, enters payment details, places order). It distinguishes itself from sibling tools like 'sephora_add_to_basket' or 'sephora_view_basket' by focusing on the final checkout process rather than earlier shopping stages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool (to complete a purchase) and includes explicit guidance about the 'dry_run' parameter for testing. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, though the distinction is implied by the tool's focus on checkout.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 well by mentioning the authentication requirement, but it doesn't describe what happens if credentials are invalid, whether there are rate limits, what format the rewards information is returned in, or any error conditions. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with just two sentences that pack substantial information. The first sentence comprehensively lists all retrievable data, and the second sentence clearly states the authentication requirement. Every word earns its place with zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (retrieving multiple types of rewards data), lack of annotations, and no output schema, the description does an adequate job but has clear gaps. It covers the purpose and authentication well, but doesn't describe the return format, error handling, or behavioral constraints that would be needed for the agent to use it effectively without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (email and password) with clear descriptions about when they're required. The description adds marginal value by reinforcing the authentication context ('Requires Sephora account credentials if not already logged in'), but doesn't provide additional syntax, format, or usage details beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve') and resource ('Beauty Insider rewards information'), with detailed enumeration of what information is included (points balance, tier status, available rewards, recent activity, tier benefits). It distinguishes itself from sibling tools like sephora_add_to_basket or sephora_search_products by focusing exclusively on rewards data rather than shopping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when credentials are needed ('Requires Sephora account credentials if not already logged in'), which helps the agent understand authentication prerequisites. However, it doesn't explicitly state when to use this tool versus alternatives or provide exclusion criteria, leaving some ambiguity about its specific use cases relative to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 return data (product names, brands, prices, ratings, URLs) and the discovery purpose, but lacks details on rate limits, authentication needs, pagination, or error handling. For a search tool with no annotations, this is adequate but leaves behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first states purpose and returns, the second provides usage guidance. It's front-loaded with key information and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 100% schema coverage, and no output schema, the description is mostly complete for a search tool. It covers purpose, returns, and usage context, but lacks output format details (e.g., structure of returned data) and behavioral traits like limits or errors, which would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no specific parameter semantics beyond implying a search functionality. Baseline 3 is appropriate as the schema handles parameter details, though the description could have added context like example queries or category usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'search' and resource 'Sephora beauty products', specifying it returns product names, brands, prices, ratings, and URLs. It distinguishes from siblings like sephora_get_product (detailed view) and sephora_add_to_basket (basket operations) by emphasizing discovery before detailed actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'Use this to discover products before viewing details or adding to basket', which implicitly guides when to use this tool versus sephora_get_product or sephora_add_to_basket. However, it doesn't explicitly state when not to use it or mention alternatives like sephora_get_rewards for rewards-related searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 optional SKU ID selection for variants, which adds useful context about behavior, but lacks details on permissions, error handling, or what happens if the product is out of stock. For a mutation tool with zero annotation coverage, this leaves gaps in behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero waste. The first sentence states the purpose, and the second provides essential usage guidance, making it front-loaded and efficiently structured without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description adequately covers the tool's purpose and usage in context with siblings. However, as a mutation tool, it could benefit from more behavioral details like confirmation of success or error scenarios, leaving minor gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning the SKU ID for variants, but doesn't provide additional syntax or format details beyond what the schema specifies. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add a product') and resource ('to the Sephora shopping basket'), specifying it's for adding items to a shopping cart. It distinguishes from siblings like sephora_get_product (for finding products) and sephora_view_basket (for viewing the basket).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use this tool ('Add a product to the Sephora shopping basket') and provides a clear alternative ('Use sephora_get_product first to find available variant SKU IDs'), guiding the agent on proper workflow and tool sequencing.
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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