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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
      Not graded
      quality
      B
      maintenance
      Connect AI agents to SQL databases (SQLite, PostgreSQL, MySQL) with a unified interface for querying data, exploring schemas, inserting rows, and exporting results to CSV. Includes safety features like dangerous query blocking and write guards.
      12 npm
      39 PyPI
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables LLMs to safely explore, query, diagnose, and modify relational databases through a unified set of schema introspection, SQL execution, and administration tools. Supports multiple database backends with AST-level read-only guards, automatic LIMIT injection, and explicit opt-in write permissions for DML and DDL.
      MIT
    • A
      license
      A
      quality
      D
      maintenance
      Enables AI agents to safely interact with MySQL/MariaDB databases, supporting read-only queries by default with optional write operations and access control.
      8
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to interact with relational databases such as MySQL, PostgreSQL, SQLite, SQL Server, and Oracle. Provides tools for querying, data inspection, and schema analysis with optional read-only mode.
      7 npm
      1
      MIT

    TDQS

    A4.6/5.0

    Scored across 10 tools

    Disambiguation5/5

    Each tool targets a distinct stage of database exploration or querying, and descriptions explicitly state when to use one over another (e.g., sample vs query, count vs query, explain vs query). The only overlap is that query can technically perform many tasks, but the guidance makes boundaries clear.

    Naming Consistency3/5

    Names mix plural nouns (connections, databases, tables), verbs (describe, sample, count, query, explain), and one snake_case verb_noun (search_schema). While readable, there is no consistent pattern, so agents cannot rely on naming alone to infer tool type.

    Tool Count5/5

    Ten tools is well-scoped for a database MCP, covering connection discovery, schema browsing, data sampling, counting, querying, and performance analysis. Each tool has a clear role, and no tool feels superfluous.

    Completeness5/5

    The surface covers the full read-only query lifecycle from connections and databases to tables, schema search, sampling, counting, querying, and explain plans. Writes are possible via query when enabled, and no major operations for the stated domain are missing.

    Maintenance

    ActivityNo data
    ResponsivenessNo issues