Anumana
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- AlicenseNot gradedqualityCmaintenanceEnforces safety and governance for SQL queries executed by AI agents, providing read-only enforcement, cost estimation, and audit trails.Apache 2.0
- AlicenseAqualityDmaintenanceEnables 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.537 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables 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
- AlicenseAqualityBmaintenanceEnables 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.3MIT
- AlicenseAqualityAmaintenanceLets 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.456 PyPI1MIT
- AlicenseAqualityAmaintenanceEnables 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.3Apache 2.0
TDQS
Scored across 9 tools
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.
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.
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.
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.