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dataparity

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    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to compare database tables using read-only tools, with credentials managed locally and never exposed to the model.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Gives an AI agent a database's tables, columns, value lists, null shares and joins from a dbprint snapshot committed to your repo, with no database connection or credentials. Tools list and search tables and columns, render prompt-ready context for a table, resolve a filter value to its stored spelling, and report what changed since the last snapshot.
      7
      353 PyPI
      2
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Exposes read-only tools that let AI assistants detect schema drift, analyze lock impact, verify data with checksums, and plan safe PostgreSQL migrations that are validated by deterministic checks and shadow runs.
      31 npm
      Apache 2.0
    • A
      license
      A
      quality
      B
      maintenance
      Exposes tools that let AI coding agents browse a repo's schema snapshot of tables and columns and validate their SQL against it before running or writing it, so queries referencing invented or stale table and column names get caught and corrected up front. Provides list_tables, describe_table, search_columns, and check_sql over the same snapshot used by the CLI, hook, and CI check.
      4
      1
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides a semantic intelligence layer for SQL Server databases, enabling AI agents to understand schema structure, relationships, and safely interact through policy-gated tools.
      3
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Exposes local data profiling, PII classification, and plan-based cleansing against DuckDB, Postgres, or SQLMesh tables through four tools (list_tables, profile, propose_cleansing_plan, apply_cleansing_plan). All reasoning stays aggregate-only — format signatures, counts, and match rates rather than raw cell values — and cleaning plans execute only after validation against a fixed operation vocabulary and explicit confirmation.
      92 PyPI
      MIT