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slicequeue

K-MFDS FoodDB MCP Server

by slicequeue
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.7

  • Disambiguation5/5

    The two tools have completely distinct purposes: one lists nutrient component types, the other searches for food nutrition data. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern: getNutritionComponentTypes and searchFoodNutrition, using clear prefixes.

    Tool Count2/5

    With only 2 tools, the server feels very thin for a food nutrition database domain. A typical scope would require at least 4-5 tools for basic CRUD and search.

    Completeness2/5

    Missing essential operations like retrieving full nutrition details for a specific food, filtering by nutrient, or adding/updating data. Users can list types and search, but cannot retrieve individual food details.

  • Average 3.2/5 across 2 of 2 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 is failing

  • This repository is licensed under ISC 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?

    No annotations are provided, so the description must disclose behavior. It only states a read operation (search) but does not mention data freshness, rate limits, authentication needs, or any side effects, leaving significant 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 concise (two sentences), directly states the purpose, and lists example criteria without unnecessary words. It is well-structured for quick understanding.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With 9 parameters, no output schema, and a sibling tool, the description is insufficient. It does not explain how parameters interact, pagination behavior, or the structure of results, leaving the agent without essential context for correct usage.

    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?

    The input schema has 100% description coverage, so the parameters are fully documented. The description redundantly mentions a few parameters but adds no meaningful explanation beyond the schema. Baseline 3 is appropriate.

    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 searches food information from a specific database and lists search criteria (food name, manufacturer, category). It is directive and informative, but does not differentiate from the sibling tool getNutritionComponentTypes.

    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 the sibling tool or how to choose between search options. It lacks explicit context about its role in a workflow.

    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 for behavioral traits, but it only states the purpose. No mention of side effects, authorization, or data freshness. The simplicity of the tool partly mitigates this, but there is no extra context beyond the basic action.

    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 a single concise sentence in Korean. It contains no wasted words, but it could be more structured (e.g., bullet points). However, for the simplicity, it is efficient.

    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 simplicity (no parameters, no output schema), the description is minimally adequate. It tells what the tool does but does not explain the return format or any usage notes. Slightly more context would improve completeness.

    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?

    There are no parameters in the input schema, and schema coverage is effectively 100%. The instructions set a baseline of 4 for zero-parameter tools, and the description adds no parameter info (none needed). This 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 retrieves a list of nutrition component types from a specific database. The verb 'retrieve' and resource 'nutrition component types' are specific, and the sibling tool 'searchFoodNutrition' suggests a different purpose, so it distinguishes well.

    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?

    No guidance on when to use this tool versus alternatives. While the sibling tool implies a different use case, the description doesn't explicitly state when to choose this over searchFoodNutrition or any exclusions.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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