GitHub - sqlsure/sqlsure: Semantic inspector for SQL — catches fan-out double-counting, additivity violations, wrong join keys, and policy breaches before the query runs. Found real bugs in the BIRD/Spider text-to-SQL benchmarks.

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.

How it works 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. Quick start pip install sqlsure 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 Three doors, one engine 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. The rules (v0.1)

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. Trust properties

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 query text; never connects to a database No 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

Where the rulebook comes from

dbt (works today): manifest.json or schema.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 or information_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 loaders in integrations/): OSI demonstrated on the spec's published examples; WrenAI MDL demonstrated on WrenAI's own shipped example manifest — primaryKey → grain, relationship joinType + condition → join edges, cube measures → additivity

Cube, Snowflake Semantic Views — adapters on the roadmap; the engine only ever sees one SemanticModel

Validated on

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.md Spider + BIRD gold queries — the zero-noise external audit above

Learn more

docs/EVIDENCE.md — what it does for you, every claim linked to a rerunnable measurement docs/ARCHITECTURE.md — how it physically works, ELI5 → god level, with real intermediate outputs docs/FOR-DUMMIES.md — every concept from zero docs/INTEGRATIONS.md — GitHub Action, pre-commit, MCP, Snowflake UDF / Cortex Agent tool, query-history audit docs/MCP.md — MCP server documentation CONTRIBUTING.md — adding rules and loaders

Apache-2.0 · sqlsure.ai

mcp-name: io.github.sqlsure/sqlsure