AI writes your SQL. sqlsure makes sure it's right.
A query can be perfectly valid, run without error, and return a number that's silently wrong — revenue double-counted by a join, an average summed, a patient identifier exposed. Databases don't catch this. Linters don't catch this. LLMs reviewing their own SQL don't catch this.
sqlsure does — deterministically, in 0.1 ms, before the query runs.
Proof, not promises:we ran sqlsure over the gold answers of the two benchmarks every text-to-SQL model is graded on.2,568 expert-written queries, 45 flags, zero false alarms— including a BIRD dev gold answer that is[provably wrong by 8×]from the exact bug class sqlsure targets, and a schema defect[now filed upstream].
sqlsure judges SQL against facts your team already declared — dbt unique
tests become grain, relationships
tests become join cardinality, one-line
meta
tags mark what's safe to sum. No new language to learn, no model to maintain by hand. Rules are dictionary lookups, not LLM calls: same input, same verdict, every time, offline.
Every rejection carries a machine-actionable fix
, so AI agents self-repair: draft → check → fix → check → execute. In our benchmark, applying the fix verbatim produced a passing query 10/10 times.
pip install sqlsure
python
from sqlsure import SemanticModel, check
violations = check(sql, model) # [] means semantically safe
Or clone and run the 30-second demo:
python check.py # 5 wrong queries rejected, 1 approved — with fixes
python -m sqlsure.scan path/to/dbt-repo --report report.md # audit any dbt repo
1. CI gate — blocks the merge when a PR double-counts:
python -m sqlsure.cli --model model.json query.sql # exit 1 on violations
2. MCP server — your AI agent must pass inspection before executing:
claude mcp add sqlsure -- python -m sqlsure.mcp_server --model /abs/path/model.json
See docs/MCP.md for tool reference and agent-loop patterns.
3. Library — embed check()
inside any text-to-SQL product or agent framework. A drop-in SemanticGate wraps Vanna/WrenAI-style generators; a semantic eval metric scores NL2SQL output where execution-accuracy is blind.
| Rule | Severity | Catches |
|---|---|---|
| FANOUT | error | SUM/COUNT of additive measure after one-to-many join |
| CHASM | error | two+ fan-out joins multiplying each other |
| ADDITIVITY | error | SUM of a non-additive measure (rates, averages) |
| SEMI_ADDITIVE | error | balances/censuses summed across their snapshot dimension |
| JOIN_KEY | error | join on columns matching no declared relationship |
| CROSS_JOIN | error | join with no predicate |
| WEIGHTED_AVG | warning | AVG silently re-weighted by fan-out |
| UNDECLARED_JOIN | warning | join with no declared relationship (unverifiable ≠ safe) |
| SENSITIVE_COLUMN | policy | PHI/PII column exposed in query output |
When sqlsure can't verify something, it says "can't verify" — never "looks fine." Honest uncertainty is a feature.
Deterministic— same SQL + same rulebook = same verdict, always; rules are dictionary lookups, auditable line by line** Offline**— zero network calls;** your SQL never leaves your machine**No data access**— parses querytext; never connects to a databaseNo telemetry— nothing collected, ever (SECURITY.md)** Supply chain**— releases ship exclusively via PyPI Trusted Publishing (OIDC) from tagged commits with public CI runs; two runtime deps
dbt(works today):manifest.json
orschema.yml
— the tests teams already wrote become enforceable semantics, zero config -
Plain PK/FK declarations(works today — powered the benchmark audits) -
The live database itself(works today): no semantic layer at all?sqlsure.introspect
builds the rulebook from the catalog — SQLite PRAGMAs orinformation_schema
PK/FK (postgres/mysql). Introspecting BIRD's own database files recovered 2 foreign keys missing from the benchmark's published schema (bird-bench/mini_dev#37)
from sqlsure.introspect import model_from_sqlite
model = model_from_sqlite("app.db") # PK -> grain, FK -> join edges
Hand-written JSON—model.example.json -
OSI and WrenAI MDL(working s inintegrations/):OSIdemonstrated on the spec's published examples;WrenAI MDLdemonstrated on WrenAI's own shipped example manifest —primaryKey
→ grain, relationshipjoinType
+condition
→ join edges, cube measures → additivity - Cube, Snowflake Semantic Views — adapters on the roadmap; the engine only ever sees one
SemanticModel
16/16 rule tests, 100% recall / 0% false positives on the paired benchmark (docs/METRICS.md)Real production repos(Mattermost's warehouse, Fivetran packages, dbt's jaffle shop) —docs/TEST-REPORTS.mdSpider + BIRD gold queries— the zero-noise external audit above
docs/EVIDENCE.md— what it does for you, every claim linked to a rerunnable measurementdocs/ARCHITECTURE.md— how it physically works, ELI5 → god level, with real intermediate outputsdocs/FOR-DUMMIES.md— every concept from zerodocs/INTEGRATIONS.md— GitHub Action, pre-commit, MCP, Snowflake UDF / Cortex Agent tool, query-history auditdocs/MCP.md— MCP server documentationCONTRIBUTING.md— adding rules and s
Apache-2.0 · sqlsure.ai
mcp-name: io.github.sqlsure/sqlsure