@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.

• 27 tools across 3 categories (dbt, quality, meta) — 21 primitive tools + 5 aggregations + 1 meta
• 4 MCP Prompts for triage workflows
• 5 aggregation tools that replace 3-5 round-trips of "list / get / list"
• extractFields response projection on high-volume reads
• 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

Category	Tools	Purpose
dbt	15 + 3 aggregations	Parse manifest.json / run_results.json / sources.json / catalog.json
quality	6 + 2 aggregations	Query quality_checks and quality_score_daily (BQ or PG); per-tier rollup via dq-tier-by-source
meta	1 (always on)	search-tools for natural-language tool discovery

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

Tools at a glance

dbt (15 + 3)

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, dbt-sla-status

quality (6 + 2)

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

Prompts

Prompt	Use when
investigate-failed-tests	"What's broken in the last 24h?"
freshness-degradation-triage	"Are any sources stale?" (Tier 1 focus optional)
dq-trend-report	"Give me a stakeholder-friendly DQ trend report"
incident-triage	"Triage <model | source>" — bundles all signals

Environment variables

Env	Required	Notes
DBT_PROJECT_DIR	yes	dbt project root (where dbt_project.yml lives)
DBT_TARGET_DIR	no	Defaults to $DBT_PROJECT_DIR/target
DBT_RUN_HISTORY_DIR	no	Optional dir for archived run_results.json history
DQ_BACKEND	no	bigquery (default) or postgres
DQ_RESULTS_TABLE	no	FQN of the checks table; required only for checks-based quality tools
DQ_SCORE_TABLE	no	FQN of the score-daily table; required for score-only tools
GOOGLE_APPLICATION_CREDENTIALS	no	For BigQuery backend (ADC fallback supported)
BQ_PROJECT_ID	no	Explicit BQ project (otherwise inferred from ADC)
PG_CONNECTION_STRING	no	When DQ_BACKEND=postgres (secret)
DQ_SCHEMA	no	generic (default) or us-all — base schema preset for the quality category
DQ_COL_*	no	Per-column overrides on top of DQ_SCHEMA (see below). Overrides must be simple SQL identifiers.
DQ_TIER1_TARGET_PCT	no	Tier 1 SLA threshold for dq-tier-status when no tier column is configured (default 99.5). Superseded by DBT_SLA_CONFIG_PATH tier_sla.1 if both are set.
DBT_SLA_CONFIG_PATH	no	Optional YAML path with tier_sla and dbt_sla blocks. Drives dq-tier-status thresholds and dq-tier-by-source per-tier targets. Mtime cached.
DBT_ALLOW_WRITE	no	Reserved for future write tools (none currently)
DBT_TOOLS / DBT_DISABLE	no	Category toggles

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.

Per-column overrides — DQ_COL_*

If your DQ tables don't match either preset, layer per-column overrides on top of DQ_SCHEMA. Any DQ_COL_* env var, when set, replaces the preset value for that single column. Unset vars keep the preset default.

Overrides are validated as simple SQL identifiers to avoid injecting raw SQL through environment variables. Table names in DQ_RESULTS_TABLE / DQ_SCORE_TABLE are also validated and quoted for the configured backend.

Env var	Logical concept	Generic preset	us-all preset
DQ_COL_RUN_AT	timestamp/date on the checks table	run_at	run_date
DQ_COL_CHECK_TYPE	check type / dimension family	check_type	check_type
DQ_COL_STATUS	pass/fail/warn/error	status	status
DQ_COL_DATASET	dataset / source / schema	dataset	source
DQ_COL_TABLE_NAME	table or target name	table_name	target_name
DQ_COL_SEVERITY	severity / dimension	severity	dimension
DQ_COL_FAILURE_COUNT	numeric failure count / metric	failure_count	metric_value
DQ_COL_MESSAGE	free-text or JSON message	message	details::text
DQ_COL_CHECK_NAME	natural identifier of the check	check_name	(none)
DQ_COL_SCORE_DATE	date column on the score table	score_date	run_date
DQ_COL_SCOPE	scope/tenant column on score table	scope	(none)
DQ_COL_TIER	tier column on score table	tier	(none)

For the three nullable columns (DQ_COL_CHECK_NAME, DQ_COL_SCOPE, DQ_COL_TIER), set the value to none / null / - to declare "no native column":

• Without check_name → the tools synthesize one from check_type || ':' || table_name. dq-get-check-history then expects checkName formatted as '<check_type>:<table_name>'.
• Without scope → dq-score-trend's scope filter is ignored (with a caveat) and dq-tier-status switches to the single-overall_score path that compares against DQ_TIER1_TARGET_PCT.
• Without tier → same single-overall_score fallback.

Example — generic preset against a Postgres schema where columns happen to be named differently:

DQ_SCHEMA=generic
DQ_COL_RUN_AT=checked_at
DQ_COL_DATASET=schema_name
DQ_COL_TABLE_NAME=tbl
DQ_COL_FAILURE_COUNT=fail_n
DQ_COL_CHECK_NAME=none      # synthesize from check_type+tbl
DQ_COL_SCOPE=none           # no per-team rollup
DQ_COL_TIER=none            # use DQ_TIER1_TARGET_PCT instead

SLA config (optional) — DBT_SLA_CONFIG_PATH

Set DBT_SLA_CONFIG_PATH to a YAML file to surface project-defined tier targets and DBT SLAs to the quality tools. Schema (extra keys ignored):

dbt_sla:
  test_pass_pct: 99.0          # consumed by dbt-sla-status (test pass rate threshold)
  freshness_pass_pct: 99.5     # consumed by dbt-sla-status (source freshness pass rate threshold)

tier_sla:
  1: 99.5                      # tier-1 overall_score / per-source pass-rate target
  2: 99.0
  3: 95.0

When set, the tier_sla map drives:

• dq-tier-status — per-tier rollup compares each row's overall_score against the matching target. Without this file, hardcoded {1: 99.5, 2: 99.0, 3: 95.0} is used.
• dq-tier-by-source — per-source pass-rate is compared to the target for that source's tier (resolved from dbt sources.yml meta.tier).
• dq-tier-status no-tier-column path (us-all preset / DQ_COL_TIER=none) — uses tier_sla.1 as the single target. DQ_TIER1_TARGET_PCT env still works as a fallback when no SLA file is set.

The dbt_sla block drives:

• dbt-sla-status — computes test pass rate from latest run_results.json and freshness pass rate from sources.json, then compares each axis against dbt_sla.test_pass_pct / dbt_sla.freshness_pass_pct. Returns passPct, target, meeting per axis plus caveats when fields or artifacts are missing.

The file is mtime-cached; edits between tool calls are picked up automatically.

Per-tier rollup from quality_checks — dq-tier-by-source

For schemas where quality_score_daily has only one row per day (no per-scope/tier breakdown), dq-tier-by-source reconstructs a per-tier picture from the raw quality_checks rows. Two modes:

mode: "source" (default) — group by source/dataset column

Use when each row of quality_checks represents a check on a source group and the dataset/source column carries the dbt source-group name directly.

1. Builds a source_name -> tier map from the dbt manifest's sources.<source>.<table>.meta.tier (first table's tier per source group).
2. Groups quality_checks rows by the dataset/source column and computes pass rate per source over a date or sinceHours window.
3. Looks up each source's tier and target (from SLA config or defaults), reports meeting / missing per tier.

mode: "table" — group by table_name column

Use when the dataset/source column is a category (bq / dbt / airflow) and the actual dbt source-table identifier lives in the table_name / target_name column as <source_group>.<table>. Common in checks tables that consolidate signals from heterogeneous backends.

1. Builds a <source_group>.<table> -> tier map from the manifest using each source entry's source_name + name + meta.tier — picks up table-level tier overrides naturally.
2. Groups quality_checks rows by the table_name column. Pre-filter via sourceFilter (e.g. sourceFilter: "bq") when only some categories produce parseable target names.
3. Each rollup key is parsed as <source_group>.<table>; rows without a . or whose key is not in the manifest land in caveats[].

Untiered rows (no manifest meta.tier) and unparseable rows always appear in caveats[] so you can tier them or accept the gap.

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.