DataQBLOCK
Open-source data quality monitoring: checks across warehouses, lakehouses and object stores, triggered by ADF, Airflow and dbt, with alerting and an MCP server.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
Data quality monitoring built on Great Expectations — checks your tables and files, alerts the team that owns them, and reacts to the pipelines that load them.
- 📖 Docs:
- Status:
v1.2.0released 2026-10-04 — changelog · tracker - License: MIT
What it does
- Checks
GX expectations (including custom SQL), freshness / volume / schema-drift / anomaly monitors and cross-dataset comparison. A column profiler and dry-run work on every datasource. Snowflake checks can also run as native DMFs, and Unity Catalog checks as Databricks DQX. Feature matrix →
- Assets, lineage & incidents
Each table or file rolls up its health across suites, with table- and column-level lineage (dbt, OpenLineage, warehouse-native) and its open incidents. Concepts →
- Quality by dimension
Every check is classified (completeness, validity, timeliness, ...), so the scorecard shows what isn't watched, not just what passes.
- Automated coverage
System-owned suites over the asset inventory, plus a review queue of suggested rules.
- Run anywhere
Run now, on a cron schedule (DST-aware), or from a pipeline trigger in ADF, Airflow or dbt. A pipeline stage can also ask DataQ for a pass/fail verdict through the pipeline gate. Orchestration →
- Alerting
Warn / fail / critical tiers → Teams, Slack, email or a webhook, with dedup, snooze and plain-language failure summaries. [Notifications →](https://theurgicduke771.github.io/DataQ/docs/latest/gui
a52c0dbcf40cOBSERVED · 2026-10-12Connect
Built from this server's own package name, version and transport as found in its source — not copied from anyone's documentation, so it cannot drift against a page we do not control. Replace the environment placeholders with a token scoped to the least it needs.
claude mcp add dataq-frontend --env DATAQ_ALERT_SMTP_PASSWORD=${DATAQ_ALERT_SMTP_PASSWORD} --env DATAQ_LLM_LIVE_API_KEY=${DATAQ_LLM_LIVE_API_KEY} --env DATAQ_ROLE_TOKENS_PATH=${DATAQ_ROLE_TOKENS_PATH} --env DATAQ_SMTP_PASSWORD=${DATAQ_SMTP_PASSWORD} -- npx -y [email protected]{
"mcpServers": {
"dataq-frontend": {
"command": "npx",
"args": [
"-y",
"[email protected]"
],
"env": {
"DATAQ_ALERT_SMTP_PASSWORD": "${DATAQ_ALERT_SMTP_PASSWORD}",
"DATAQ_LLM_LIVE_API_KEY": "${DATAQ_LLM_LIVE_API_KEY}",
"DATAQ_ROLE_TOKENS_PATH": "${DATAQ_ROLE_TOKENS_PATH}",
"DATAQ_SMTP_PASSWORD": "${DATAQ_SMTP_PASSWORD}"
}
}
}
}Exposed tools (2)
2 read · 0 write · 0 destructive.
| Tool | Risk | Description |
|---|---|---|
orders-suite | read | imported |
orders-suite-2 | read | old desc |
Trust audit
BLOCKgrade F · trust 47/100 Do not install this without reading the findings. The audit found something that could harm you or your machine.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | FAIL |
| L2 | Instruction surface (what it tells the agent) | FAIL |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- declared (5 observation(s))
- Network
- declared (5 observation(s))
- Shell
- declared (2 observation(s))
- Dependencies
- not all pinned
- Secrets in source
- found
Findings (25)
document = yaml.load(text, Loader=_DocumentLoader) # noqa: S506 # nosec B506
description: Run the end-of-week security scan (CONTRIBUTING rule 36) — Dependabot + secret-scanning alerts, local pip-audit/pnpm-audit mirror of the CI gates, betterleaks sweep, OWASP spot check on e
module = importlib.import_module(module_name)
assert captured["uri"] == "postgresql://catalog_user:db-pw@localhost:5432/catalog"
"postgresql://user:pw@localhost:5432/dataq",
"iceberg", {"catalog_uri": "postgresql://u:OLD_PW@h:5432/cat"}, target"iceberg", {"catalog_uri": "postgresql://u:NEW_PW@h:5432/cat"}, target"postgresql://user:hunter2@host/db", # a DSN with a password
mockCreate.mockResolvedValue({ ...KEY, token: 'dq_live_ab12THE_FULL_SECRET' });.betterleaks.toml
.betterleaksignore
.env.app.example
.pre-commit-config.yaml
.env.app.prod.example
"../../etc/passwd",
("dev", "../../etc/passwd"),docs_catalog.read_page("../../../etc/passwd")const marketing = resolve(here, '../../marketing');
const OUT = fileURLToPath(new URL('../../docs/site/assets/screenshots/', import.meta.url));store = FakeSecretStore({"wh": "https://evil.example.com/exfil"})_publisher({_WS_NAME: "https://evil.example/exfil"}).publish(webhook="https://169.254.169.254/latest/meta-data",
assert svc.is_safe_generic_webhook_url("https://169.254.169.254/latest/meta-data") is False7. **Webhook hostility** — POST to `/api/v1/orchestration/events/<provider>` for every provider in `ORCHESTRATION_PROVIDERS` (`backend/app/db/models.py`) with: missing/wrong auth (secret/HMAC per prov
_URL_A = "https://8.8.8.8/hook-a"
Gates applied: no_behavioural_pass.
a52c0dbcf40cfull audit observations/trust-audit/mcp-server/theurgicduke771__dataq.json · Report an issue / request a re-scanAudit history
Every audit this server has had. A grade with a past is a grade somebody is still checking.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-12 | a52c0dbcf40c | BLOCK | F | 47 | first audit |
Questions
What is the DataQ MCP server?
Open-source data quality monitoring: checks across warehouses, lakehouses and object stores, triggered by ADF, Airflow and dbt, with alerting and an MCP server.
What tools does DataQ expose?
2 in total: 2 read-only, 0 that write, and 0 that can delete or overwrite. Every one is listed on this page with its risk.
Is DataQ safe to connect to an agent?
No — not without reading the findings first. The audit graded it F (47/100) and found 2 critical or high issues in the source. Each one is listed on this page with the file and line it is on.
What credentials does DataQ need?
It reads DATAQ_ALERT_SMTP_PASSWORD, DATAQ_LLM_LIVE_API_KEY, DATAQ_ROLE_TOKENS_PATH, DATAQ_SMTP_PASSWORD, DATAQ_WEBHOOK_SECRET, KV_SECRET_CONN_SNOWFLAKE_DEV_FINANCE, OPENBAO_TOKEN, PERF_SF_AUTH_TYPE, PERF_SF_SECRET, PERF_UC_SECRET, REDSHIFT_TEST_ADMIN_SECRET and REDSHIFT_TEST_READER_SECRET from the environment. Give it a token scoped to the least it needs — an agent that can be talked into calling a tool can be talked into calling it with your credentials.
How does DataQ run?
It speaks streamable-http, so it runs as a service you connect to over the network. It is published on npm as dataq-frontend at 0.1.0.
How current is this page?
The grade is for one exact copy of the source (a52c0dbcf40c), read on 2026-10-12. The repository is watched and re-audited when it changes.