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MasaBigBrother

jp-address-mcp

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion. The tool's purpose is unambiguous and clearly described.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (normalize_jp_address), which is predictable and consistent with common conventions.

    Tool Count3/5

    With only one tool, the server feels minimal for a dedicated address utility. It earns its place, but the scope is thin and borderline.

    Completeness5/5

    The tool consolidates parsing, verification, and geocoding into one operation, covering the full address normalization workflow. There are no obvious gaps for the stated purpose.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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
  • 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

  • Behavior4/5

    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 reveals that the tool splits addresses into structured fields, returns a verification flag and lat/lng, and handles full-width characters, mixed hyphens, romaji, and building/room separation. This adds meaningful behavioral context beyond the schema, though it does not cover potential error cases or side effects.

    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 and well-structured: the first sentence states the core purpose, the second elaborates on outputs, and the third covers input handling and usage. Every sentence contributes actionable information with no padding.

    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 tool with one parameter and no output schema, the description is quite complete. It explains what the tool returns (verification flag, lat/lng) and the structured fields extracted. However, it does not mention potential failure modes or how the verification flag behaves, leaving minor gaps for a normalization/verification tool.

    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?

    The schema documents the single 'address' parameter with 100% coverage, so the baseline is 3. The description adds semantic value by explaining the types of messy input accepted (full-width, hyphens, romaji) and the tool's ability to separate building/room, providing practical guidance beyond the schema's generic 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 specific verbs: 'Normalize and verify a Japanese postal address.' It details the structured output fields (prefecture, city, town, block number, building, room), verification flag, and lat/lng, effectively distinguishing it from generic parsers by mentioning its handling of messy 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?

    The description provides explicit use cases: 'Use whenever you need to register, validate, or route a Japanese address reliably.' It gives clear context for when to use it, though it does not mention when not to use it or list alternatives (likely because no siblings exist).

    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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  • Evaluate tool definition quality.

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