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PostgreSQL-Performance-Tuner-Mcp

by isdaniel
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Python

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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

    A4.4/5.0

    Scored across 14 tools

    Disambiguation4/5

    Most tools have distinct purposes, such as analyze_index_bloat for index analysis and get_slow_queries for query retrieval. However, some overlap exists between analyze_table_bloat and get_bloat_summary, which both focus on bloat analysis but at different granularities, potentially causing minor confusion.

    Naming Consistency5/5

    All tool names follow a consistent snake_case pattern with clear verb_noun structures, such as analyze_query, get_active_queries, and find_unused_indexes. This uniformity makes the tool set predictable and easy to navigate.

    Tool Count5/5

    With 14 tools, the server is well-scoped for PostgreSQL performance tuning, covering key areas like bloat analysis, query optimization, and health checks. Each tool serves a specific function without redundancy, fitting the domain appropriately.

    Completeness5/5

    The tool set provides comprehensive coverage for performance tuning, including analysis (e.g., bloat, queries), monitoring (e.g., health, active queries), and optimization (e.g., index recommendations, hypothetical testing). No obvious gaps are present, supporting full lifecycle management.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive

    Resources