cost-guard-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query Postgres, BigQuery and DuckDB read-only through policy-checked SQL, with AST-based guards that refuse writes and dangerous functions, column masking on the way out, and an audit log of every query. Also exposes schema browsing and query-planning tools so agents can see allowed tables, columns and costs before running anything.MIT
- 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.31Apache 2.0
- AlicenseAqualityBmaintenanceEnables AI coding agents to preflight SQL, NoSQL, graph, warehouse, and vector queries against real schemas or pasted DDL, estimating cost/risk, suggesting verified rewrites, and explaining plans before execution. It supports 12 database engines and catches costly query patterns without running the query.938 PyPIMIT
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TDQS
Scored across 4 tools
Each tool has a clearly distinct role: bounded execution, cost estimation, engine capability disclosure, and credential/connectivity checking. The boundary between estimate_query_cost and run_query_bounded is explicit (estimate vs. execute with caps), and the dependency notes reinforce rather than blur the distinction.
All four names follow a consistent snake_case verb_noun pattern (run_query_bounded, describe_engine_capabilities, estimate_query_cost, check_credentials). No mixed conventions or vague standalone verbs.
Four tools is well-scoped for a cost-guard server: estimate, bounded execution, capability disclosure, and credential verification each earn their place. No redundant or filler tools.
Core cost-control lifecycle is covered: check credentials, understand engine accuracy, estimate, then execute with caps. Minor gaps exist around discovering configured engines/warehouses or retrieving post-execution actual cost history, but agents can work around these via existing tools.