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MCP PostgreSQL Operations

by call518
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Python
Hybrid

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    TDQS

    A3.9/5.0

    Scored across 34 tools

    Disambiguation3/5

    The tools have clear descriptions but significant overlap exists in monitoring areas. For example, get_autovacuum_activity, get_autovacuum_status, get_vacuum_analyze_stats, and get_vacuum_effectiveness_analysis all focus on vacuum operations with subtle distinctions that could confuse agents. Similarly, multiple I/O and statistics tools (get_io_stats, get_table_io_stats, get_index_io_stats, get_database_stats) have overlapping purposes despite targeting different objects.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with 'get_' prefix, making them predictable and readable. The naming convention is uniformly applied across all 34 tools, with clear descriptive names that indicate their purpose (e.g., get_table_bloat_analysis, get_replication_status).

    Tool Count2/5

    34 tools is excessive for a PostgreSQL monitoring server, creating cognitive overload. While PostgreSQL has many monitoring aspects, the tool set feels bloated with redundant tools (e.g., four vacuum-related tools) and could be consolidated. A more focused set of 15-20 tools would better serve the domain.

    Completeness4/5

    The tool set provides comprehensive monitoring coverage for PostgreSQL administration, including connections, performance, replication, vacuum, bloat, and configuration. Minor gaps exist in areas like session management (e.g., no tool for session termination) and real-time query execution, but core monitoring workflows are well-covered.

    Maintenance

    ActivitySlowing
    ResponsivenessUnresponsive

    Resources