Blue Perfumery MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation4/5
Most tools have distinct purposes: get_by_id, get_by_category, list_all, and search target different retrieval methods, while get_purchase_link is unique. However, list_all_perfumes and search_perfumes could overlap in some cases if an agent wants to browse versus search, but descriptions clarify their intents.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case, using verbs like 'get', 'list', and 'search' appropriately. The naming is predictable and readable across all five tools.
Tool Count5/5With 5 tools, this server is well-scoped for a perfumery collection, covering essential retrieval operations without being overly complex. Each tool serves a clear purpose, making the count appropriate for the domain.
Completeness3/5The toolset is read-only, focusing on retrieval and search, which is reasonable for a catalog server. However, it lacks any write operations (e.g., create, update, delete) or advanced features like filtering by price or rating, which could limit agent functionality in broader e-commerce contexts.
Average 3.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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 the full burden of behavioral disclosure. It states the search functionality but doesn't describe any behavioral traits such as response format, pagination, rate limits, authentication needs, or error handling, which are critical for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded in a single sentence, with zero wasted words. It directly communicates the core functionality without unnecessary elaboration, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like result format, limitations, or error cases, which are essential for a search tool. The simplicity of the tool (one parameter) doesn't compensate for these gaps in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'query' fully documented in the schema as 'Search query for perfume name or brand'. The description adds no additional meaning beyond this, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('search') and resource ('perfumes'), and specifies the search criteria ('by name or brand'). However, it doesn't explicitly differentiate from sibling tools like 'list_all_perfumes' or 'get_perfumes_by_category' in terms of search methodology versus listing/filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 to prefer this over 'list_all_perfumes' for broader queries, 'get_perfumes_by_category' for categorical filtering, or 'get_perfume_by_id' for known IDs, leaving usage context unclear.
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 the full burden of behavioral disclosure. It only states what the tool does ('Get a specific perfume'), without mentioning any behavioral traits such as error handling, permissions required, rate limits, or what happens if the ID doesn't exist. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks behavioral context and usage guidelines, which are important for a tool that retrieves specific data by ID.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'id' parameter documented as 'The perfume ID'. The description adds no additional meaning beyond this, as it doesn't explain the ID format, source, or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('a specific perfume by its ID'), making the purpose unambiguous. However, it doesn't differentiate from siblings like 'get_perfumes_by_category' or 'list_all_perfumes' beyond the 'by ID' specificity, which is why it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like 'list_all_perfumes' or 'search_perfumes'. The description implies usage when you have a specific ID, but it doesn't explicitly state this as a guideline or mention exclusions.
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 the full burden of behavioral disclosure. It states the tool retrieves a purchase link but doesn't clarify if this is a read-only operation, requires authentication, has rate limits, or what happens with invalid IDs. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, 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.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, and output expectations, which are needed for a complete understanding despite the simple schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'id' parameter documented as 'The perfume ID'. The description adds no additional semantic context beyond this, such as format examples or validation rules. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the specific resource ('Shopier purchase link for a specific perfume'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_perfume_by_id' which might retrieve perfume details rather than purchase links, leaving some ambiguity in sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 prerequisites, such as needing a valid perfume ID, or compare it to sibling tools like 'search_perfumes' for broader queries, leaving the agent without context for tool selection.
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 the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves—such as whether it returns all perfumes at once, uses pagination, requires authentication, or has rate limits. This leaves significant gaps for a tool that presumably returns a list.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no annotations, no output schema), the description is adequate as a basic listing function. However, it lacks details on output format, potential limitations (e.g., large result sets), or how it differs from siblings, which could be helpful for an agent in a richer context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to add parameter semantics, so it meets the baseline expectation for a parameterless tool, but doesn't exceed it by explaining any implicit filtering or sorting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all perfumes in the Blue Perfumery collection'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_perfumes_by_category' or 'search_perfumes', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like 'get_perfumes_by_category' or 'search_perfumes'. It doesn't mention use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
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?
No annotations are provided, so the description carries the full burden. It only states what the tool does without disclosing behavioral traits such as whether it's a read-only operation, potential rate limits, authentication needs, or what the output format looks like. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('Get perfumes by category') and provides essential examples in parentheses. There is zero waste, and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter with full schema coverage) but lack of annotations and output schema, the description is minimally adequate. It covers the basic purpose but fails to provide necessary context about behavior, output, or usage guidelines, leaving gaps for an AI agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'category' fully documented in the schema including its enum values. The description adds no additional meaning beyond what the schema provides, such as explaining the semantics of 'niche' or how categories are defined. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'perfumes', specifying the filtering criteria 'by category' with examples. It distinguishes from siblings like 'list_all_perfumes' by indicating category-based filtering, though it doesn't explicitly contrast with 'search_perfumes' which might offer broader search capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when filtering perfumes by category, but it doesn't explicitly state when to use this tool versus alternatives like 'search_perfumes' or 'list_all_perfumes'. No exclusions or prerequisites are mentioned, leaving some ambiguity about optimal use cases.
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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