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walkingzzzy

lvke-mcp

by walkingzzzy
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Python
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Server Quality Checklist

67%
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 possibility of confusion or overlap between tools. Every tool has a clearly distinct purpose by default.

    Naming Consistency5/5

    With a single tool, there is no pattern to violate. The name 'echo' is simple, clear, and directly describes its behavior.

    Tool Count1/5

    The server has only one trivial tool (echo), which is far too few to provide any useful functionality. This is an extreme mismatch between the tool count and any conceivable practical scope.

    Completeness1/5

    The server only echoes input and offers no other operations. The surface is severely incomplete, lacking any meaningful domain coverage or lifecycle support.

  • Average 3.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 72 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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    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

  • Behavior1/5

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

    This is a clear annotation contradiction. The description states the tool echoes the message parameter as-is, implying a pure read-only operation with no side effects. However, the annotations set readOnlyHint=false, indicating the tool may not be read-only. This direct conflict confuses the agent and warrants the lowest score.

    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 a single short sentence that conveys the core behavior with no wasted words. It is front-loaded with the key information and appropriately sized for a trivial tool.

    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?

    While the tool is simple and the description states its function, the conflicting annotation creates ambiguity about side effects. Additionally, there is no mention of return format or edge cases. Given the annotation contradiction, the contextual information is incomplete and potentially misleading.

    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 input schema has no property descriptions (0% coverage), so the description must compensate. It does so by explicitly naming the 'message' parameter and stating it is echoed unchanged. This adds meaning beyond the schema's bare type/name and sufficiently explains the parameter's role.

    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 function: it echoes the message parameter as-is. The verb '回显' (echo) and resource 'message 参数' make the purpose unambiguous. With no sibling tools, differentiation is unnecessary.

    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 is provided on when to use this tool or what alternatives exist. The description only explains what the tool does, leaving usage context entirely implicit. For an echo tool, usage may be obvious, but the rubric requires explicit or clearly implied guidance.

    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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Glama performs regular codebase and documentation scans to:

  • 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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