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asgard-ai-platform

mcp-tw-lvr

by asgard-ai-platform
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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 or overlap.

    Naming Consistency5/5

    With a single tool, the naming is trivially consistent; the snake_case format is clear.

    Tool Count3/5

    One tool is borderline; while it serves a specific query purpose, the server feels too minimal for rich interaction.

    Completeness3/5

    The tool covers multiple query types, but lacks any CRUD operations or auxiliary functions, leaving notable gaps for a data service.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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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 discloses key behaviors: year adjustment (Minguo), default date range, query types, output normalization via lvr.adapter.normalize, and the raw option. No mention of authentication, rate limits, or side effects, but as a read-only query tool, these are less critical. Overall transparent.

    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 brief and well-structured: first sentence states purpose, then explains year defaults, query types, and output format. Every sentence adds value with no redundant information. Ideal length for quick understanding.

    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 10 parameters, 0% schema coverage, no annotations, but an existing output schema, the description covers the most critical aspects (year, query_type, raw) but omits details on location parameters. It provides enough context for a typical use case, though more detail on city/town/road would improve completeness.

    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 description coverage is 0%, requiring compensation. The description explains years (Minguo, defaults), query_type (enum meanings), and raw (key naming). However, it does not describe city, town, road, building, start_month, end_month parameters, leaving them partially undocumented. Adds meaningful context for some but not all parameters.

    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 it queries Taiwan's real price registration data (查詢台灣實價登錄資料), specifies the year system (Minguo), query types, and output format. It is specific and distinguishes itself from potential similar tools, though no siblings exist.

    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?

    It provides guidance on year defaults (last year start to this year end if not specified), explains each query_type option with Chinese translations, and notes the raw flag for original keys. No explicit exclusions or alternative tool references, but context is clear for an isolated tool.

    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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