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25andresbernal

product-feedback-mcp

by 25andresbernal
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Python
Hybrid

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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

    A4.3/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: search, theme listing, theme detail, severity tagging, severity summary, and problem statement drafting. No two tools overlap in functionality, and descriptions make selection unambiguous.

    Naming Consistency5/5

    All tools follow the same verb_noun snake_case pattern (search_feedback, list_themes, get_theme, tag_severity, severity_summary, draft_problem_statement). The naming is consistent and predictable.

    Tool Count5/5

    Six tools is well within the ideal range for a focused product-feedback analysis server. Each tool covers a distinct stage of the analysis workflow, and none feel redundant or missing.

    Completeness4/5

    The tool surface covers the core analysis lifecycle: search, theme discovery, theme inspection, severity tagging, aggregate severity, and problem statement generation. A direct get_feedback_item is missing but search_feedback already returns full item details, so this is a minor gap.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources