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iapke486-arch

daejeon-mcp-server

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity. An agent will always select the correct tool.

    Naming Consistency5/5

    The single tool uses a consistent verb_noun pattern (daejeon_search_restaurants) with no conflicting conventions.

    Tool Count3/5

    A single tool is minimal but acceptable for a focused search server. The tool provides comprehensive filtering options within its scope.

    Completeness5/5

    The tool covers the entire domain of restaurant searching in Daejeon, including category filtering and keyword search, with pagination support. No missing operations are apparent for a search-only service.

  • Average 4.6/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 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
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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries full burden. It transparently discloses that keyword filters are applied client-side after the API response, not server-side, and notes monthly data updates. While it does not cover rate limits or authentication, the most critical behavioral trait (client-side filtering) is clearly communicated.

    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 Korean paragraph that covers all necessary information. It front-loads the purpose and then details filtering behaviors. While it is fairly long, every sentence is informative and adds value. A slight structural improvement could separate each keyword behavior, but it remains effectively concise.

    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 adequately lists returned fields (store name, address, phone, hours, category, menu details) and update frequency. It addresses parameter behavior and paging advice. Missing aspects include error handling or handling of empty results, but overall it is sufficiently complete for a search 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 coverage is 100%, baseline 3. The description adds significant value by differentiating server-side (category) vs. client-side (keywords) filtering, explaining that keywords perform partial matching within the current page, and recommending to increase numOfRows for broader searches. This goes well beyond the 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?

    The description clearly states the tool queries a list of operating restaurants in Daejeon, listing specific data fields (name, address, phone, hours, category, menu, etc.) and update frequency. It distinguishes itself by explaining filtering behaviors, which differentiates it from potential sibling tools.

    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?

    The description explicitly explains the server-side filtering for 'category' (exact match) and the client-side, partial-match nature of 'nameKeyword', 'addressKeyword', and 'menuKeyword'. It advises increasing 'numOfRows' or paging for broad searches, providing clear guidance on when and how to use each parameter.

    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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