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molpass

sajuMCP

by molpass

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity. The single tool has a clear and specific purpose.

    Naming Consistency5/5

    With a single tool, naming is inherently consistent. The name 'calculate_saju' clearly indicates its function.

    Tool Count4/5

    One tool for a specialized domain like saju is appropriate. The tool is comprehensive, covering many aspects in one calculation, so the count feels sufficient.

    Completeness5/5

    The tool appears to cover all major aspects of saju calculation: original chart, ten gods, 12 luck, harmony, gods, and various luck cycles. No obvious gaps.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • 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 of behavioral disclosure. It describes the tool as a computation that outputs various saju components, but it does not explicitly state that the tool is read-only or has no side effects. Given the nature of the tool (calculation), the lack of explicit safety guarantees is a minor gap. The description does mention how gender affects Daewoon direction, which adds some 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 extremely concise, consisting of only two sentences. It front-loads the core purpose and output components, then adds a specific usage guideline. Every sentence earns its place, with no redundant or filler content.

    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?

    For a complex tool with 12 parameters and no output schema, the description provides a comprehensive list of output components (원국, 십성, etc.), which gives the agent a clear understanding of what the tool returns. It also includes guidance on the gender parameter. However, it could mention the expected date format or input validation, but the schema covers some of that. Overall, it is quite complete given the tool's nature.

    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 provides descriptions for all 12 parameters (100% coverage), so the description adds little additional meaning for parameters. The description reiterates the importance of gender (similar to schema) and lists output components, but does not clarify parameter semantics beyond what the schema already offers. Baseline 3 is appropriate 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?

    The description clearly states the tool's purpose: calculating saju/manse (사주/만세력) from birth details. It lists the specific components output (e.g., 원국, 십성, 12운성 etc.), making the tool's function unmistakable. The title and name align, and there are no sibling tools to differentiate from.

    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 explicitly recommends providing gender for accurate results ('gender는 대운 방향에 영향을 주므로 정확한 결과를 위해 제공을 권장한다'), which is a clear usage guideline. It also implies that hour, minute, calendar type, etc., have defaults. However, it does not explicitly state when not to use the tool or provide alternative tools, but given the absence of siblings, this is acceptable.

    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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