dbprint

A new engineer onboards once. Your agent onboards every session.

Your agent reads your migrations and your ORM models, so it gets the shape right and nothing looks broken. Then it spends twelve tool calls learning that country has four values, and does it again tomorrow.

What it never learns is what a colleague would have said in thirty seconds: revenue_cents is gross and every report uses the net column, orders joins shipments one-to-many more often than anyone expects, legacy_uid has been dead since the migration. It doesn't know that's missing.

So write the onboarding doc. Half of it won't sit still: cardinality and null rates are stale the day you commit them. The other half no database can produce. dbprint measures the numbers and gives you somewhere to write the rest.

Both land in your repo as plain text. Your agent reads them offline — no connection, no credentials.

A print is a portable, git-committed snapshot of a database's structure and column-level data distributions, consumable offline by humans, AI coding agents, and CI. The on-disk format — not the CLI — is the durable artifact here: specified, versioned independently of the tool, and designed for other producers to emit.

What a print looks like

An excerpt, some fields elided, from the reference print in this repo:

row_count: 2500
columns:
  catalogue_url:
    sql_type: character varying(200)
    nullable: false
    null_rate: 0.0
    classification: text
    cardinality: 2500
    cardinality_ratio: 1.0
    inferred:
      looks_like: url
      sampled: 1000
      matched: 1000
      candidate_key: true
    values:
    - value: https://specimens.example.org/accession/1
      count: 1
    - value: https://specimens.example.org/accession/10
      count: 1
    # ... 28 more
    values_coverage: 0.012      # top 30 of 2500 - a value list, not a claim of completeness
    distribution: long_tail

  provenance_country:
    sql_type: character(2)
    classification: categorical
    cardinality: 10
    inferred:
      looks_like: country_code
      sampled: 10
      matched: 10
    values:
    - value: AU
      count: 250
    - value: CA
      count: 250
    - value: DE
      count: 250
    - value: FR
      count: 250
    - value: GB
      count: 250
    # ... 5 more
    values_coverage: 1.0        # the list is the whole domain, not a sample
    distribution: uniform

  received_at:
    sql_type: timestamp(0) with time zone
    classification: temporal
    range:
      min: '2018-01-08T00:00:00Z'
      max: '2024-11-28T00:00:00Z'
      span_days: 2516
    percentiles:
      p01: '2018-02-09T00:00:00Z'
      p25: '2019-10-02T00:00:00Z'
      p50: '2021-06-19T00:00:00Z'
      p75: '2023-03-05T00:00:00Z'
      p99: '2024-10-25T00:00:00Z'

values_coverage: 1.0 is the load-bearing field: those ten values are the entire domain, not the top ten. An eleventh value can be ruled out without asking the database. (When a table is profiled from a sample rather than in full, coverage is measured over the rows that were actually read — see Scoped profiling below.)

inferred is what dbprint concluded rather than read: looks_like: country_code is a verdict, and sampled/matched are the evidence for it, so a shape confirmed by ten values never looks like one confirmed by ten thousand.

Alongside it, relationships.yaml carries the join graph:

refers_to:
- column:
  - collector_id
  target_table: seedbank.collector
  target_column:
  - collector_id
  on_delete: RESTRICT
  detection: declared      # or `inferred`, when dbprint guessed the edge from naming
  observed:
    fanout_avg: 6.25
    target_coverage: 1.0

That half is measured. The other half you write, in the same directory, in the same commit — statistics.annotations.yaml for a column:

columns:
  collector_id:
    note: >-
      FK to collector.collector_id. Field collectors are affiliated with a
      partner institution; collector.institution names it.

and description.md for table-level narrative that no statistic can express.

dbprint never writes to either file, and both are read alongside the statistics — but on anything the statistics can answer, the statistics win; the narrative is authoritative only on what a number can't.

Quickstart

Requires Python 3.13+. Install with the driver extra for your database:

pip install dbprint[postgres]     # or [mysql], [snowflake], [duckdb], [clickhouse], [redshift], [databricks], [bigquery]

The Postgres adapter shells out to pg_dump for DDL, so the PostgreSQL client binaries must be on your PATH.

Scaffold a project in your repo root:

dbprint init

That writes .dbprint.yaml (commit it), creates prints/, and writes a credentials template to ~/.dbprint/connections.yaml — outside the repo, so secrets never sit next to the config.

Put your connection details in the credentials file:

# ~/.dbprint/connections.yaml
primary:
  host: localhost
  port: 5432
  database: my_db
  user: dbprint_ro
  password: change_me

and tell .dbprint.yaml which adapter to use and which tables to read:

# .dbprint.yaml
connections:
  primary:
    adapter: postgres
    auto: true
    include:
      - "public.*"

Credentials resolve from DBPRINT_<CONN>_<KEY> environment variables first, then ~/.dbprint/connections.yaml, then a project-root .env. Every key of both files is in docs/CONFIG.md.

Profile it:

dbprint generate

Commit the result alongside your code. Then inspect it with no database in reach:

dbprint list

What lands in your repo

prints/<connection>/
  manifest.yaml                    # inventory, freshness, per-table thresholds
  reading.md                       # generated guide - how to read the rest of the print
  diff.yaml                        # machine-readable drift from the last run
  manifest.annotations.yaml        # optional, connection-level notes you write
  <schema>/<table>/
    ddl.sql                        # native DDL, normalized for stable diffs
    statistics.yaml                # column distributions
    relationships.yaml             # declared + inferred foreign keys
    description.md                 # optional, you write it
    statistics.annotations.yaml    # optional, per-column notes you write
    relationships.annotations.yaml # optional, per-edge notes you write

reading.md is written fresh on every run: it teaches a consumer which fields to trust for what, so a print explains itself to an agent that has never seen the format.

The four .md / .annotations.yaml files are yours — dbprint never overwrites them, and all four are folded into what agents read.

Commands

Full reference: docs/CLI.md.

Connecting an agent

An MCP client launches the server itself, so it — not you — picks the working directory. Name the project explicitly:

{
  "mcpServers": {
    "dbprint": {
      "command": "uvx",
      "args": ["--from", "dbprint[mcp]", "dbprint", "serve", "--project", "/srv/analytics"]
    }
  }
}

The server reads the committed print and never connects to a database, so the client needs no credentials.

Reading a remote project

--project also accepts a git address, so a print committed to someone else's repository reads without cloning it by hand first:

dbprint list --project https://github.com/acme/demo
dbprint context seedbank.accession --project git@github.com:acme/demo.git#main:srv/analytics

dbprint clones it for you and asks for no credentials of its own — your existing SSH agent or credential helper covers a private repository. It's read-only: commands that write or connect live to a database (generate, diff, check --online) refuse a remote locator; every other command works against it exactly as it does locally.

Beyond the basics

• Semantic inference. Every column gets one of 8 classifications. Text, categorical and foreign-key columns get one of 32 shape patterns (uuid, email, phone, ip, country_code, ...), published with the sample size and match count behind the verdict. Columns that hold data that must not leave the database get a sensitivity axis (personal_name, postal_address, national_id, credential, ...). Columns effectively unique (cardinality ratio >= 0.9999) are marked candidate_key, with a candidate_key_exception alongside whenever that threshold admits measured or estimated duplicates rather than true uniqueness. Foreign keys your schema never declared are inferred from naming and stamped detection: inferred.
• Redaction. Withhold cell values with mask, drop, or hash (salted), targeting columns by glob or by inferred sensitivity or by shape — so a personal_name column added next quarter is covered by a rule written today. Counts, cardinality and distributions survive intact; only literals are withheld. The salt lives with your credentials, never in the committed config.
• Assertions. Write data-quality checks in YAML against any statistic, or as raw SQL. Statistic assertions are evaluated against the committed print by plain dbprint check, with no database in reach; SQL assertions need dbprint check --online. See docs/ASSERTIONS.md.
• CI exit codes. 0 ok, 1 malformed print or unusable config, 2 stale, 3 drift, 4 connection failure, 5 partial run, 6 assertion failure, 7 total failure. diff, check and context all speak --format json|yaml.
• Scoped profiling. Per-table rules can sample (seeded from the table name, where the engine supports seeding), apply a row filter, or change what's measured — gated on table size, so one rule covers every fact table you haven't named yet. A table read this way carries a scope block and a per-column rows_scanned, and every count in the file is denominated in the rows actually read, never silently in the whole table.
• MCP server. dbprint serve exposes 5 tools — get_table_context, list_tables, search_columns, get_manifest, get_diff — plus every artifact as a resource. It reads the committed print only and never opens a database connection. See docs/MCP.md.
• Markdown skill. For clients that take markdown rules or custom instructions instead of MCP, the same guide each print ships as reading.md doubles as a drop-in skill file — no process to run. A copy sits at docs/examples/skill/ so you can read it before installing anything.
• Token-budgeted context. dbprint context seedbank.accession --budget 4000 fits the Markdown view into a token budget, dropping the lowest-priority sections first.

Closest neighbours

What dbprint is not

• Not a migration tool. It doesn't author, apply or version DDL changes. Use Alembic, Flyway, Liquibase, sqldef, Atlas.
• Not a schema-management or ORM layer. A print is generated from your database; it is never the source of truth you build it from.
• Not a live query interface. Nothing in a print executes SQL for your agent.
• Not a data-quality platform. The assertion DSL is a CI gate over a print, not monitoring, alerting or lineage. Use Soda, Great Expectations, Elementary.
• Not a data catalog. No hosted service, no shared search index, no business glossary. dbprint docs serves a local read-only view of a committed print, bound to loopback, with nothing to administer.

The format

The format is specified in docs/format/v1/SPEC.md.

Seven JSON Schemas ship inside the installed package at src/dbprint/spec/v1/ — four for the generated artifacts, three for the annotation files you write — and the conformance validator is a public API with no database, config or CLI required:

from dbprint.conformance import validate_print

issues = validate_print("prints/production")  # conforms iff no error-severity issues

The reference print at docs/format/v1/examples/ is real dbprint output, not a hand-written illustration — what you read there is what you get.

Configuration

.dbprint.yaml holds project config (committed); ~/.dbprint/connections.yaml holds credentials (never committed). Every key of both, with types, defaults and resolution order, is in docs/CONFIG.md. Internals are documented in docs/ARCHITECTURE.md.

License

Copyright 2026 Jakub Roman. Distributed under the Apache License 2.0.