spendguard 💰

Your AI agent has a company credit card and no spending limit. spendguard is the bouncer.

Give an agent a "run SQL" tool on BigQuery or Snowflake and it will happily SELECT * a billion-row table and burn $4 before you've finished your coffee. Nobody watches the meter. spendguard is a drop-in MCP server that sits between your agent and the warehouse: it previews the dollar cost before every query, enforces budgets, learns how accurate its estimates are, and suggests cheaper rewrites when a query blows the budget.

Not a previewer — a spend governor.

30-second start

uvx spendguard-mcp
# or
pipx install spendguard-mcp
`
*(Note: PyPI release (uvx spendguard-mcp) coming with v0.1.0)*

Add to your MCP client (Claude Code, Cursor, Codex, Copilot — see examples/.mcp.json.example):

{ "mcpServers": { "spendguard": { "command": "uvx", "args": ["spendguard-mcp"],
  "env": { "BIGQUERY_PROJECT": "my-project",
            "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/sa.json" } } } }

Then ask your agent:

"Using spendguard, how much will this query cost before you run it?"

// estimate_query_cost("bigquery", "SELECT * FROM proj.ds.events_2025")
{
  "accuracy_tier": "PRECISE",
  "estimated_bytes": 1409286144,
  "estimated_cost_usd": 0.0081,
  "caveats": []
}

// run_query_bounded("bigquery", "SELECT * FROM ...", "max_estimated_cost_usd": 5.0)
{ "status": "refused", "reason": "over_call_cap",
  "detail": "Estimated $12.40 exceeds your per-call cap $5.00.",
  "suggestion": "Call suggest_cheaper_query with this SQL..." }

// spend_report()
{ "bigquery": { "actual_usd": 3.21, "queries": 41 }, ... }

The tools

Accuracy tiers — we tell you how much to trust the number

Every estimate carries its tier and caveats. BigQuery RLS-masked tables report 0 bytes by design — we flag it instead of calling it free. Remote-function / ML.GENERATE_TEXT billing is excluded and flagged. Capacity-billed projects get bytes only, no fake dollars.

What makes it different

• A ledger with a memory. Every estimate is stored; actuals are reconciled post-execution (INFORMATION_SCHEMA.JOBS, Snowflake query history, system.billing.usage). The per-engine calibration factor (EWMA, α=0.3) makes heuristic estimates converge on your reality.
• Budgets with teeth. Daily/session caps, anomaly detection (flags queries >40× your rolling median), and a human-confirm flow: over-threshold queries return a single-use 5-minute token the agent must hand back.
• It fixes, not just refuses. Over-budget queries get concrete rewrites, not error messages.
• No gateway, no SaaS, no new infrastructure. One stdio process, SQLite ledger at ~/.spendguard/. It runs wherever your agent runs.

GitHub Action: cost-delta on every dbt PR

action/ is a composite action for dbt/SQL repos: it dry-runs every changed *.sql file at head and base SHAs and posts a sticky PR comment with per-file bytes, estimated USD, and the total delta — optionally failing the check over a budget. v1 is BigQuery-only, and the comment says so.

- uses: tanveer-arch/spendguard/action@v1
  with:
    gcp_project: my-project
    gcp_credentials: ${{ secrets.GCP_SA_KEY }}
    fail_on_over_cap: true
    max_delta_usd: 10

Roadmap

• Databricks fetch_actual_cost wiring against a live workspace (system.billing.usage join)
• Snowflake reconciliation via ACCOUNT_USAGE.QUERY_HISTORY
• Snowflake key-pair auth path (JWT)
• PR-comment action for Snowflake/Databricks (query-plan based)
• Per-developer attribution for team spend reports

Contributing

PRs welcome — see CONTRIBUTING.md. We keep a standing queue of good first issue / hacktoberfest tasks and aim to respond within 24 hours.

License

MIT — see LICENSE.