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code-smell-detection-mcp

by Jeon-byeong-yoon
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JavaScript
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.1/5.0

    Scored across 13 tools

    Disambiguation3/5

    Some tools have clear distinctions (e.g., list vs get, by commit vs by file), but there is notable overlap between get_code_analysis_results, get_latest_pyexamine_result, and get_pyexamine_result_by_commit, as well as between get_high_severity_smells and list_smell_findings. Descriptions help clarify, but an agent could easily confuse similar query tools.

    Naming Consistency2/5

    Naming is inconsistent: list_metric_analyses vs get_code_analysis_results use different prefixes for list operations, and run_metric_analysis vs save_smell_analysis vs analyze_python_smells have varied verb conventions. A consistent verb_noun pattern is not maintained throughout.

    Tool Count5/5

    13 tools is well-scoped for a code-smell detection server, covering analysis execution, result querying, and filtering without being overwhelming. Each tool serves a distinct analytical purpose, making the count appropriate.

    Completeness4/5

    The tool set covers analysis execution, result storage, retrieval, and filtering across metric, smell, and code analyses. Minor gaps exist, such as no direct 'run code-analysis' tool distinct from smell analysis, but core workflows are well supported.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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