cost-guard-mcp gives AI agents a pre-flight view of warehouse query impact and a way to refuse execution when caller-defined limits cannot be met. It is a Python Model Context Protocol (MCP) server that sits between an agent and an existing warehouse connection; it is not a hosted database or a guarantee that the final bill will match an estimate.
Why put a guardrail in front of an agent’s SQL tool?
A generic SQL execution tool lets an agent send a query, but it does not necessarily help the agent answer two practical questions first: “how much will this cost” and “how much data will this return?” An agent can produce plausible SQL while misunderstanding a table’s size or omitting a partition filter. A costly scan may then run, or a large result set may consume the agent’s context.
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In his article about the project, SAI RAM gives examples involving bills in the hundreds of dollars and millions of rows. Those are scenarios he describes, not independently measured rates of agent errors or warehouse overruns. The project’s purpose is narrower and more concrete: expose a query’s expected impact before execution, then let the caller put explicit bounds on execution.
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The project documents a local MCP server launched as a stdio process, with four tools for working with a supported warehouse:
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- Credential and connectivity check: verify that the configured connection can be used.
- Engine capabilities: describe the warehouse-specific features relevant to the server.
- Pre-flight estimate: estimate query impact and report an accuracy tier.
- Bounded execution: run a query subject to caller-defined limits for bytes billed, returned rows, and estimated dollar cost.
This is a thin guard layer around a warehouse connection, not a separate database gateway. The project README and PyPI listing document its packaging and supported behavior; check them for the current setup steps and engine support, which can change.
What the estimate means depends on the engine
The project distinguishes its estimates by confidence tier. Those labels describe different evidence, not a common scale on which every engine produces equivalent precision.
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| Engine | Estimate source and project tier | What the evidence means |
|---|---|---|
| BigQuery | dryRun; the project labels supported cases PRECISE. |
Google says a dry run validates a query and estimates charges in on-demand mode. In capacity mode it gives approximate bytes, not a direct dollar estimate. It does not consume query slots or incur a charge. Some cases are unsuitable for cost prediction: a federated-query dry run can report a zero-byte lower bound even if rows are returned, and row-level security can cause zero bytes to be returned to avoid a side channel. See Google Cloud’s query documentation. |
| Snowflake | EXPLAIN; the project labels the estimate UPPER_BOUND. |
Snowflake describes assigned partitions and bytes as upper-bound estimates. Runtime optimizations, such as join pruning, can reduce the actual partitions and bytes scanned. The estimate is therefore a cautionary ceiling, not a prediction that the full amount will be consumed. See Snowflake’s EXPLAIN documentation. |
| Databricks | The project documents a HEURISTIC estimate. |
This is a lower-confidence estimate based on warehouse assumptions, not a vendor-equivalent dry-run cost guarantee. The project README is the source for its implementation details and current support. |
The project README also notes that Snowflake estimates exclude Cortex AI Function credits; BigQuery capacity or Editions billing may provide byte counts without a dollar estimate; and Databricks estimates depend on warehouse assumptions. A byte figure is not always a full cost figure, and the project’s accuracy tier should be read alongside the billing model and its stated limitations.
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The project describes run_query_bounded as refusing a query when a requested bound cannot be enforced from the estimate, rather than proceeding as though missing cost information were safe. This fail-closed behavior matters when a caller requires a cost cap: absence of an estimate is not evidence that a query is within budget.
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The author also says row-bounded execution wraps the caller’s query and fetches one row beyond the cap so the tool can distinguish a result exactly at the limit from an overflow. These are implementation claims from the author’s technical article; they are not an independent live test of every engine or query shape.
A pre-flight check and execution caps reduce avoidable risk, but they do not prove the final bill in advance or prevent every costly query. Their value depends on what the engine can estimate, whether the relevant limit can be enforced, and whether the configuration reflects the real warehouse.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Configuration choices that affect the result
Match the estimate to the real warehouse
The project documentation says warehouse size and Snowflake edition can affect the displayed dollar estimate and recommends supplying values that match the actual warehouse. An omitted or incorrect size can make a displayed figure understate actual costs. Confirm billing mode and warehouse assumptions before relying on a cost threshold.
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The README documents engine-specific credentials and requires a Snowflake role rather than choosing a default role. The author describes this as a deliberate choice to avoid falling back to a broadly privileged account, and says exceptions sanitize secrets. These are design choices, not evidence of a security audit or a guarantee that credentials cannot be exposed. Use credentials scoped to the queries and data the agent needs, and configure environment variables in the environment where the MCP host launches the local server.
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Check current setup and compatibility
Follow the installation and connection instructions in the repository README and package listing, including the current engine support and credential names. Package releases, integrations, and cloud pricing behavior can change; do not assume an old configuration example or estimate policy still applies.
Where this guard layer fits
cost-guard-mcp is most useful when an agent has permission to query a warehouse but should not be trusted to infer cost and output-size consequences from SQL alone. Its central design decision is to surface an estimate with an explicit confidence tier and make bounds part of execution, rather than treating SQL execution as an unqualified tool call.
As SAI RAM puts the design principle: “An estimate that admits what it doesn’t know is more useful, and safer, than one that guesses and calls itself precise — that’s the whole idea, and it’s the same idea whether you’re reading it off a CostEstimate object or off this changelog.” That is a useful standard for evaluating the project: inspect both the estimate and its limits, then decide whether the available evidence is strong enough for the query you intend to run.
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