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    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to safely explore and query databases across SQLite, PostgreSQL, MySQL, and MSSQL, with schema introspection, natural language queries, and data profiling.
      MIT
    • A
      license
      A
      quality
      D
      maintenance
      Enables AI assistants to connect to and interact with PostgreSQL, MySQL, SQLite, and MongoDB databases through natural language, supporting schema exploration, query execution, data export, and more.
      13
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables LLM agents to connect to and retrieve data from multiple database types through a unified API, including PostgreSQL, MySQL, SQLite, BigQuery, Oracle, MS SQL, and Redshift. Supports schema normalization and query transformation for LLM-friendly database interaction.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables LLM agents to connect to and retrieve data from multiple database types through a unified API, with schema normalization, query transformation, and secure connections. Supports PostgreSQL, MySQL, SQLite, BigQuery, Oracle, MS SQL, and Redshift.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables LLM agents to connect to and query many database types—including PostgreSQL, MySQL, SQLite, BigQuery, Oracle, MS SQL, and Redshift—through a single unified API. It provides schema retrieval and optimized query execution with normalized schemas for easier natural-language-driven data access.
      -

    TDQS

    B3.3/5.0

    Scored across 13 tools

    Disambiguation4/5

    MongoDB, SQL, and Redis operations are clearly separated by tool prefixes and descriptions, so an agent can usually select the right backend. Minor overlap remains between read/query tools like mongo_find, db_query, and redis_command, and between the Mongo/SQL list/describe pairs, but the database family disambiguates them.

    Naming Consistency4/5

    Most tools follow a predictable family-prefix plus action pattern: mongo_*, db_*, and connection lifecycle verbs like open_connection/close_connection/list_connections. There are small deviations such as mongo_find vs db_query vs redis_command, but the overall scheme is readable and consistent.

    Tool Count5/5

    The 13 tools are well-scoped for a multi-database connector, with connection management plus a focused set of read, write, list, and describe operations per backend. No tool appears redundant enough to remove, and the count stays within a manageable range.

    Completeness4/5

    The surface covers connection lifecycle, SQL read/write and introspection, MongoDB query/aggregate/write/introspection, and Redis read-only commands. Gaps remain around Redis writes, SQL DDL/transaction operations, and broader MongoDB administration, but core database workflows are covered.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues