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cqfn
by cqfn

Server Quality Checklist

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

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap. The tool's purpose is clearly defined.

    Naming Consistency5/5

    Single tool, so no inconsistency. The name follows a verb_noun pattern (find_issue).

    Tool Count3/5

    One tool for a specific purpose (finding design issues in one file) is borderline but acceptable for a narrow scope.

    Completeness2/5

    The server only covers a single operation (analyze one file for one issue). Lacks features like batch processing, multiple issue types, or suggestions, leaving significant gaps for a design review tool.

  • Average 3.9/5 across 1 of 1 tools scored.

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

    • 0 of 2 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 is passing
  • 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",
      "maintainers": [
        "your-github-username"
      ]
    }

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations, the description partially discloses behavior: it analyzes one Java file, ignores cosmetic issues, and identifies the problem with its location. However, it does not specify whether the tool is read-only or if it makes changes, nor how the analysis is performed or what the output format is.

    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 at 6 sentences, front-loaded with the core action, and each sentence adds unique value: scope, focus, exclusion criteria, quality definition, and output. No redundant or unnecessary information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description is adequate for a simple tool with one parameter and no output schema, explaining the core function clearly. However, it lacks details on output format, performance expectations, or edge cases (e.g., empty file, multiple flaws), which would enhance completeness given the tool's analytical 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 only parameter is 'path', and the description clarifies it expects a Java file path, adding context beyond the bare schema. However, it does not detail the format or any constraints on the path, and schema description coverage is 0%, so the description provides minimal additional meaning.

    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 tool name and description clearly indicate the tool identifies the most serious design flaw in a Java file, emphasizing critical issues over cosmetic ones. It distinguishes itself from a general code review tool by focusing on immediate refactoring needs.

    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 specifies when to use the tool (to find critical design issues) and provides criteria for what constitutes a serious flaw (maintainability, readability, loose coupling, high cohesion). It implicitly advises against using it for minor issues, and with no sibling tools, this provides sufficient context.

    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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