@us-all/dbt-mcp

dbt MCP server — manifest.json, run_results.json, sources.json, catalog.json, plus DQ result tables (BigQuery / Postgres) behind one stdio MCP. Built on @us-all/mcp-toolkit.

A read-only window into your dbt project for LLM clients. No dbt run triggering — just deep introspection, run-history analysis, source freshness, per-column test coverage, lineage walks, and (if you have a custom DQ result table) historical check trends and Tier SLA status.

For DAG triggering / run history / log tails, install the companion @us-all/airflow-mcp alongside.

• 22 tools across 3 categories (dbt, quality, meta)
• 4 MCP Prompts for triage workflows
• 4 aggregation tools that replace 3-5 round-trips of "list / get / list"
• Read-only by default
• Hybrid backend: BigQuery (default) or Postgres for DQ result tables — both peer-imported lazily

Install

# 1. add the MCP server
pnpm add -D @us-all/dbt-mcp
# 2. add the DQ backend you actually use (only if you query custom DQ tables):
pnpm add -D @google-cloud/bigquery   # OR
pnpm add -D pg

Run

DBT_PROJECT_DIR=/path/to/dbt-project \
DQ_RESULTS_TABLE=my-project.data_ops.quality_checks \
npx @us-all/dbt-mcp

The server speaks MCP stdio; wire it into Claude Desktop / Cursor / any MCP client. Set MCP_TRANSPORT=http to opt in to Streamable HTTP transport (Bearer auth, /health endpoint).

Categories

Toggle with DBT_TOOLS=dbt (allowlist) or DBT_DISABLE=quality (denylist).

Tools at a glance

dbt (15 + 2)

dbt-list-models, dbt-get-model, dbt-list-tests, dbt-get-test, dbt-list-sources, dbt-get-source, dbt-list-exposures, dbt-list-macros, dbt-get-macro, dbt-list-runs, dbt-get-run-results, dbt-failed-tests, dbt-slow-models, dbt-coverage, dbt-graph, freshness-status, incident-context

quality (5 + 2)

dq-list-checks, dq-get-check-history, dq-failed-checks-by-dataset, dq-score-trend, dq-tier-status, failed-tests-summary, dq-score-snapshot

Prompts

Environment variables

DQ result-table schema flavors

The quality category supports two schema presets via DQ_SCHEMA:

DQ_SCHEMA=generic (default)

Columns assumed on DQ_RESULTS_TABLE: run_at, check_name, check_type, dataset, table_name, status, severity, failure_count, message.

Columns assumed on DQ_SCORE_TABLE: score_date, scope, tier, completeness_pct, freshness_pct, validity_pct, anomaly_free_pct, overall_score.

dq-tier-status rolls up by Tier 1/2/3 against the per-scope rows.

DQ_SCHEMA=us-all

Real schema used at us-all (Postgres data_ops database):

quality_checks: run_date, check_type, dimension, source, target_name, status, metric_value, threshold, details (JSONB).

quality_score_daily: run_date, completeness_pct, freshness_pct, validity_pct, anomaly_free_pct, overall_score, total_checks, failed_checks.

In this flavor quality_score_daily is one row per day (no per-scope rollup, no tier column). dq-tier-status falls back to comparing the day's overall_score against DQ_TIER1_TARGET_PCT (default 99.5).

dq-get-check-history requires checkName formatted as '<check_type>:<target_name>' since us-all has no native check_name column.

v0.2 will add per-column env vars (DQ_COL_*) for arbitrary schemas.

Tested-against schemas

• dbt manifest schema v11 / v12 / v13 (others usually parse but a caveats line will flag them)

Companion server

For Airflow DAG operations (list, runs, task instances, log tail, trigger, clear), install @us-all/airflow-mcp alongside this server.

Build

pnpm install
pnpm run build      # tsc → dist/
pnpm test           # vitest
pnpm run smoke      # spawns dist/index.js, calls initialize + tools/list (set env first)

License

MIT — see LICENSE.