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
| Tool | What it does |
|---|---|
describe_engine_capabilities |
What each engine can/can't tell you — the honesty contract, first |
estimate_query_cost |
Free pre-flight estimate, calibrated from your ledger history |
run_query_bounded |
Estimate → budget gate → execute → reconcile actual billed cost |
spend_report |
Reconciled spend per engine + calibration state |
set_budget |
Persist a daily/session cap or confirm-above threshold |
suggest_cheaper_query |
Concrete rewrites: LIMIT injection, partition filters, SELECT * guidance |
Proven live
We tested the full governor loop end-to-end on live BigQuery with the public bigquery-public-data.samples.shakespeare dataset. The dry-run returned a PRECISE estimate of 1,332,943 bytes (≈ $0.000008). The actual billed cost came back ~8× higher (≈ $0.00006) — entirely because BigQuery enforces a 10 MB minimum per query. After reconciliation, the ledger auto-calibrated the BigQuery engine factor from 1.0 → 3.06 in a single query.
How it works
agent
│
▼
┌──────────────────────────────────────────────┐
│ spendguard │
│ │
│ 1. estimate ─ free dry-run, $ figure │
│ 2. budget gate ─ refuse / rewrite if over │
│ 3. execute ─ run on warehouse │
│ 4. reconcile ─ actual billed cost │
│ 5. ledger ─ update calibration │
└──────────────────────────────────────────────┘
│
▼
warehouse
(BigQuery · Snowflake · Databricks)
Accuracy tiers — we tell you how much to trust the number
| Engine | Tier | How |
|---|---|---|
| BigQuery | PRECISE | Free dry run → exact bytes scanned × $6.25/TiB |
| Snowflake | UPPER_BOUND | EXPLAIN USING JSON plan → largest byte figure as bound; dollars assume one 60s minimum billing window |
| Databricks | HEURISTIC | No dry-run API exists — warehouse size × plan-shape runtime × $/DBU, then calibrated against system.billing.usage actuals over time |
Every estimate carries its tier and caveats. BigQuery enforces a 10 MB minimum billing per query — any scan under 10 MB is billed as 10 MB (the estimate will carry a caveat). 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_costwiring against a live workspace (system.billing.usagejoin) - 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.
Metadata
Release files for spendguard-mcp 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| spendguard_mcp-0.1.1.tar.gz | 30.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| spendguard_mcp-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 63.4 kB
Release files / spendguard_mcp-0.1.1.tar.gz
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|---|---|
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Signed by GitHub Actions, verified by PyPI on Oct 4, 2026.
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