SommelierX Wine Pairing MCP

Server Quality Checklist

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Latest release: v1.1.2
Disambiguation5/5
Each tool has a clear and distinct purpose: three pair wine with different inputs (meal, ingredients, recipe URL), one finds meals for a wine, one does group pairing, and two are search tools for discovering meals and ingredients. There is no confusing overlap.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., pair_wine_with_meal, search_ingredients). However, there is some inconsistency: 'find_meals_for_wine' and 'group_pairing' use different verbs, though they remain descriptive. Overall, the naming is clear.
Tool Count5/5
With 7 tools, the server covers all core wine pairing use cases without being bloated. The count is well-scoped for its domain, earning each tool its place.
Completeness5/5
The tool set covers the full range of pairing needs: wine-to-dish, dish-to-wine, ingredients-to-wine, recipe-to-wine, and multi-dish group pairing, plus discovery. No obvious gaps for its intended purpose.
Average 4.3/5 across 7 of 7 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
- Last stable release on April 30, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It discloses read-like behavior (search) and mentions auth and pricing, but does not cover pagination, rate limits, or exact side effects. Adequate but not exhaustive.
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 concise sentences plus succinct best-use and auth/pricing lines. No filler, front-loaded with purpose.
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?
Missing output schema, but description mentions return fields (names, IDs, groups). For a simple search tool with few parameters, this is largely sufficient. Could benefit from hinting at result format.
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?
Input schema has 100% coverage with descriptions for both parameters. Description adds little beyond repeating parameter examples (e.g., 'mushroom'). Baseline of 3 is appropriate.
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?
Description clearly states it searches for ingredients in a specific database and returns names, IDs, and groups. It differentiates itself from sibling tools like pair_wine_with_ingredients by positioning itself as a discovery tool.
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?
Provides a best-use example ('What mushroom ingredients are available?') and explicitly directs to use before pair_wine_with_ingredients. Mentions authentication and pricing, but lacks explicit guidance on when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description takes on the full burden of behavioral disclosure. It details authentication methods (API key or x402 payment) and cost ($0.01/call), and notes that the server searches the database. These go beyond the schema and help the agent understand side effects and requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise with two main sentences plus a line for 'Best for', auth, and price. It efficiently communicates the core functionality, though the extra details could be separated for readability.
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 the simplicity of the tool (2 parameters, no output schema), the description covers purpose, usage, auth, and cost. It does not describe the output format in detail, but the phrase 'top 5 wine matches' provides enough context for the agent to anticipate the result.
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 coverage is 100%, so baseline is 3. The description adds example values for parameters but does not provide additional semantic depth beyond what the schema already specifies (e.g., language defaults to 'en').
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 tool's purpose: 'Find the best wine pairings for a specific dish or meal.' It provides concrete examples ('risotto ai funghi', 'grilled salmon') and specifies the output ('top 5 wine matches'). This distinguishes it from siblings like 'pair_wine_with_ingredients' which pair by ingredients, not meal name.
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 includes a 'Best for' section ('What wine goes with this dish?') that clarifies the primary use case. It does not explicitly list when not to use the tool or mention alternatives, but the provided context is sufficient for correct usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses authentication (API key or x402 payment), pricing ($0.02/call), Pro requirement, and expected outputs (recipe name, ingredients, top 5 wine matches). It could mention error handling but overall provides sufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise, front-loading the purpose in one sentence and then providing additional details. The line about authentication and price is useful but could be better formatted. Still efficient overall.
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 output schema, the description explains the return format. It covers authentication, pricing, and input requirements. The tool has only 2 parameters, so the description is adequately complete, though it lacks error handling details.
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 coverage is 100%, with both parameters already described in the schema. The description adds no new semantic value beyond restating the URL and language expectations, so a baseline score of 3 is appropriate.
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 tool takes a recipe URL, extracts ingredients, and finds wine pairings, listing specific outputs. It distinguishes from sibling tools like 'pair_wine_with_ingredients' and 'find_meals_for_wine'.
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 a use case ('Best for: What wine goes with this recipe?') and notes Pro tier requirement and pricing. However, it does not explicitly specify when not to use it or mention alternatives for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses auth methods (API key or USDC payment) and pricing ($0.01/call). As a read operation with no annotations, description provides useful behavioral context beyond schema.
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?
Very concise: two sentences plus auth/pricing line. Front-loaded with purpose, no redundant phrases. Every sentence adds value.
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?
Complete for a simple two-parameter tool with no output schema: explains input, output, auth, and pricing. Sibling names provide additional context.
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 has 100% coverage with descriptions. Description adds minor context (examples, default language) but core info is already in schema. Baseline 3 due to high schema coverage.
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?
Description clearly states it finds dishes pairing with a specific wine, with examples ('Barolo', 'Chardonnay') and output details (top 10 dishes with scores). Differentiates from siblings like 'pair_wine_with_ingredients' by focusing on dishes.
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?
Includes a 'Best for' example (`I have a Barolo, what should I cook?`) indicating when to use. No explicit alternatives or when-not statements, but context implies it's for dish matching, not ingredient or meal pairing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, description discloses authentication (API key or x402 payment) and pricing ($0.005/call). Mentions return fields. Lacks details on pagination or limits, but adequate for a simple 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, then usage context, then best-for/auth/pricing. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, return fields, usage context, authentication, pricing, and an example. No output schema needed; description is complete for a simple search tool.
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 coverage is 100% so baseline is 3. Description adds example query context but does not significantly enhance parameter meaning beyond schema descriptions.
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?
Clearly states verb (Search), resource (meals/dishes in SommelierX database), and return fields (names, IDs, descriptions). Provides example query. Explicitly distinguishes from sibling tools by mentioning usage before pair_wine_with_meal or group_pairing.
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?
Explicitly states when to use: 'Use this to discover available meals before using pair_wine_with_meal or group_pairing.' Provides best-for example. Does not explicitly state when not to use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries burden. It discloses auth requirements, pricing, and ingredient name resolution. But lacks details on error handling, output structure beyond 'top 5 wine matches with scores', and limitations.
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?
Concise, front-loaded with purpose, includes essential auth and pricing info in few sentences. No wasted words.
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 or output schema, description covers purpose, input format, auth, and cost. Could elaborate on output structure and edge cases, but adequate for a straightforward tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all parameters with descriptions. Description adds value by explaining natural language input and automatic resolution, plus example format and default language.
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 'find' and resource 'wine pairings' for a list of ingredients. It distinguishes from siblings like pair_wine_with_meal and pair_wine_with_recipe_url by specifying ingredients as input.
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?
Provides explicit use case: 'Best for: "What wine goes with these ingredients?"' and mentions auth and pricing. However, it does not mention when not to use or alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully covers behavioral traits: auth requirements (API key or x402), pricing ($0.03/call), tier requirement (Pro), and input constraints (2-10 meals). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, packing purpose, usage, requirements, and pricing into a few sentences. It is front-loaded with the primary purpose, making it efficient for an AI agent to parse.
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 output schema, the description does not specify the return format (e.g., list of wines with scores). However, it covers all key operational details (inputs, auth, pricing) and is adequate for a tool that likely returns similar structure to siblings.
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?
Schema coverage is 100%, so the schema already describes parameters. The description adds value by stating the input range ('2-10 meal names') and providing an example, which aids understanding beyond the schema's formal description.
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 tool's purpose with a specific verb and resource: 'Find the best wine that pairs well with multiple dishes at once.' It distinguishes from siblings by focusing on multiple dishes, contrasting with single-dish tools like pair_wine_with_meal.
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 explicit usage context, including a best-use example ('What single wine works for a 3-course dinner?') and requirements (Pro tier, auth methods, pricing). It lacks explicit when-not-to-use guidance, but the context is clear.
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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