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Postgres MCP Pro

by moecodeshere
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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

    B3.3/5.0

    Scored across 9 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no ambiguity: analyze_db_health focuses on health checks, analyze_query_indexes and analyze_workload_indexes target index optimization from different sources, execute_sql and explain_query handle query execution and analysis, get_object_details and list_objects provide object-level information, get_top_queries identifies performance issues, and list_schemas lists schemas. There is no overlap in functionality.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern using snake_case: analyze_db_health, analyze_query_indexes, analyze_workload_indexes, execute_sql, explain_query, get_object_details, get_top_queries, list_objects, and list_schemas. The naming is predictable and readable throughout.

    Tool Count5/5

    With 9 tools, the count is well-scoped for a Postgres database management server. Each tool earns its place by covering distinct aspects like health analysis, query optimization, execution, explanation, object listing, and performance monitoring, without being excessive or sparse.

    Completeness4/5

    The tool surface is nearly complete for Postgres database management, covering health checks, query analysis, execution, object inspection, and performance monitoring. Minor gaps exist, such as lack of tools for database creation, user management, or backup operations, but core workflows are well-covered and agents can work around these omissions.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources