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postgres-mcp

by JaviMaligno
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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

    A4/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no ambiguity. For example, describe_table focuses on table structure, while table_stats provides statistical metrics, and list_constraints is separate from list_indexes. The descriptions clearly differentiate overlapping concepts like query (read-only) vs execute (write operations).

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with snake_case throughout. The naming convention is perfectly predictable: list_tables, describe_table, search_columns, explain_query, etc. There are no deviations in style or convention across the 14 tools.

    Tool Count5/5

    14 tools is well-scoped for a PostgreSQL database management server. Each tool earns its place by covering distinct aspects of database interaction: schema exploration, table analysis, query execution, and metadata inspection. The count aligns perfectly with the comprehensive but focused domain coverage.

    Completeness5/5

    The tool surface provides complete coverage for PostgreSQL database interaction. It includes schema listing, table/view description, query execution (both read and write), performance analysis (explain_query), metadata inspection (constraints, indexes, functions), and search capabilities. No obvious gaps exist for typical database exploration and management workflows.

    Maintenance

    ActivitySlowing
    ResponsivenessUnresponsive

    Resources