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    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      Enforces safety and governance for SQL queries executed by AI agents, providing read-only enforcement, cost estimation, and audit trails.
      Apache 2.0
    • A
      license
      A
      quality
      D
      maintenance
      Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.
      5
      37 npm
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to pre-flight estimate query costs, enforce hard budgets, reconcile actual billed spend, and suggest cheaper rewrites across BigQuery, Snowflake, and Databricks.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables agents to validate, explain, and gate SQL statements against a supplied schema before execution, returning safe/review/blocked verdicts, issue codes, and fix suggestions. It acts as a pre-execution go/no-go guard and composes with database access servers.
      3
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Lets AI agents estimate the cost and result size of BigQuery and Snowflake queries before they run, then execute them only within per-call byte, row, and dollar bounds. Estimates are labeled with an accuracy tier so agents never over-trust an approximate figure.
      4
      56 PyPI
      1
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Enables AI agents to query Trino and Apache Pinot lakehouses under enforced governance, where every SQL statement is AST-validated, table-allowlisted, priced from the engine's own plan before it runs, and blocked or admitted against scan-byte and intermediate-row budgets. It also grounds agents with schema discovery tools, returns verified results with warnings instead of misleading answers, and records every tool call on an audit trail.
      3
      Apache 2.0

    TDQS

    A4.1/5.0

    Scored across 9 tools

    Disambiguation3/5

    The set has a dense cluster around cost-checking a query: preflight_query, suggest_query, and rewrite_query all assess/verify query cost and rewrite behavior, making it non-obvious which to call first (descriptions do disambiguate via the generate->check->refine framing). The preflight_* trio is well-separated by scope (SQL vs vector vs offline DDL), and explain_query_working is distinct as a teaching tool.

    Naming Consistency3/5

    Most names follow a verb_noun pattern (list_targets, list_policies, rewrite_query, suggest_query) and the preflight_* prefix is consistent. However, describe_schema_tool adds a redundant '_tool' suffix and explain_query_working ends in '_working', breaking the pattern in two places.

    Tool Count5/5

    Nine tools is well-scoped for a query cost-analysis/enforcement server, with each tool covering a recognizably distinct surface (targets, policies, three preflight variants, rewrite, explanation, schema, grading). No bloat or thinness.

    Completeness4/5

    The domain (analyze/grade/rewrite queries without executing them) is covered end-to-end: discover targets, inspect schema, learn policies, preflight SQL/vector/offline, rewrite, and explain. Minor gaps exist, e.g. no explicit connectivity/health probe or standalone equivalence-verification tool, but core workflows are complete.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues