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Postgres MCP Pro

by sparta2025
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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

    A3.7/5.0

    Scored across 15 tools

    Disambiguation4/5

    Most tools target clearly distinct concerns: schema listing, object details, SQL execution, explain plans, active queries, locks, sizes, and index analysis. The main potential confusion is between analyze_db_health and get_database_info, which both provide broad database-level overviews and could lead an agent to pick the wrong one.

    Naming Consistency5/5

    All tool names follow a consistent lowercase verb_noun pattern (list_schemas, get_object_details, execute_sql, analyze_index_performance, etc.). The naming convention is uniform and predictable, making it easy to infer what each tool does.

    Tool Count4/5

    15 tools is at the upper end of the ideal range but still reasonable for a Postgres server covering introspection, SQL execution, performance analysis, and health checks. The inclusion of manage_encryption_key and list_tools adds some scope beyond the core database domain, making the set feel slightly over-packed.

    Completeness4/5

    The tool surface covers schema discovery, object details, arbitrary SQL execution, query explanation, performance diagnostics, active queries, locks, table sizes, and database info. Notable gaps include no query cancellation/termination, no vacuum/analyze maintenance tool, and no explicit database-level listing, though execute_sql can work around some of these.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources