metabase-database-metadata（元数据库 数据库元数据）

Metabase Database Metadata Format

Metabase represents database metadata — synced databases, their tables, and their fields — as a tree of YAML files. Files are diff-friendly: numeric IDs are omitted entirely, and foreign keys use natural-key tuples like ["Sample Database", "PUBLIC", "ORDERS"] instead of database identifiers.

The format is defined by a specification bundled alongside this file as spec.md (upstream source: metabase/database-metadata). The same project ships a CLI (@metabase/database-metadata on npm) that converts the raw JSON from GET /api/database/metadata into the YAML tree described by the spec.

Canonical layout

All metadata for a project lives under a top-level .metabase/ directory:

• .metabase/databases/ — the YAML tree. This is the canonical source for the agent. Read these files to understand the schema, columns, types, and FK relationships.
• .metabase/metadata.json — the raw API response. Potentially multi-megabyte (or multi-gigabyte) JSON with flat databases / tables / fields arrays. Never open, grep, or pass it to tools. It exists only as input to the extractor.

The .metabase/ directory and the .env file described below should both be gitignored. On large warehouses the extracted metadata can reach gigabytes — committing it would make the repo painful or unusable.

First-time setup

Do not run any of the steps below proactively at session start. Only run them when the user explicitly asks to fetch metadata, set up the workflow, or requests something that plainly requires knowledge of the database schema (e.g. "write a query against ORDERS", "describe what tables exist").

When setup is triggered:

1. Ensure a .env file with credentials

Check whether .env exists at the repo root and contains both METABASE_URL and METABASE_API_KEY.

• If .env is missing:

- If .env.template exists, ask the user to copy it and fill in the values. - If neither exists, create .env.template with placeholders and ask the user to create .env from it: METABASE_URL=https://metabase.example.com METABASE_API_KEY=

• If .env exists but is missing one of the required variables, ask the user to add it.

Do not invent, guess, or hardcode credentials. Always ask.

2. Ensure .env and .metabase/ are gitignored

Read the repo's .gitignore and confirm both .env and .metabase/ are listed. If either is missing, ask the user before modifying .gitignore — e.g.:

.env and .metabase/ are not in .gitignore. Committing them would leak credentials or bloat the repo (metadata can be gigabytes). Shall I add them?

Only edit .gitignore after the user confirms.

3. Fetch and extract

Once .env is valid and ignore rules are in place:

set -a; source .env; set +a

mkdir -p .metabase
curl -sf "$METABASE_URL/api/database/metadata" \
  -H "X-API-Key: $METABASE_API_KEY" \
  -o .metabase/metadata.json

rm -rf .metabase/databases
npx @metabase/database-metadata extract-metadata .metabase/metadata.json .metabase/databases

Then read the YAML tree under .metabase/databases/ to answer the user's question.

Session start behaviour

At the start of a session, do not run any fetch commands. Just observe what's on disk:

• If .metabase/metadata.json and .metabase/databases/ both exist, assume the tree is sufficiently up to date and use it directly. Do not refetch.
• If the tree is missing or only partial, do nothing until the user asks for something that needs it — then fall into the first-time-setup flow above.

If something in the tree looks stale or inconsistent while you're using it, mention it to the user and let them decide whether to refetch. Never refresh silently.

Refreshing (user-initiated only)

If the user explicitly asks to refresh metadata, re-run step 3 from first-time setup. Always remove .metabase/databases before re-extracting so stale files are not left behind.

Entities

Three entity types, two file types:

Foreign keys

Foreign keys use natural-key tuples, not numeric IDs:

• Database FK: the database name (string) — e.g. "Sample Database"
• Table FK: [database, schema_or_null, table] — e.g. ["Sample Database", "PUBLIC", "ORDERS"]
• Field FK: [database, schema_or_null, table, field,...nested_field_names] — e.g. ["Sample Database", "PUBLIC", "EVENTS", "DATA", "user", "name"] for a JSON-unfolded column DATA.user.name

Field-level FKs show up as parent_id (nested field parent) and fk_target_field_id (referenced PK for FK columns).

Type attributes on fields

• database_type — the raw native type string from the driver (BIGINT, VARCHAR, JSONB, etc.). Database-specific.
• base_type — the Metabase type matching the native type (type/BigInteger, type/Text, type/Structured, etc.).
• effective_type — the type Metabase treats the column as at query time. Only emitted when it differs from base_type (i.e. coercion is configured).
• coercion_strategy — the rule producing effective_type from base_type (e.g. Coercion/ISO8601->DateTime, Coercion/UNIXMilliSeconds->DateTime).
• semantic_type — business-domain label (type/PK, type/FK, type/Email, type/Category, type/Latitude, etc.). Drives UI and some analytical behavior.

See the extracted spec for the full type hierarchy and available coercion strategies.

Reading the spec

This skill ships with a local snapshot of the spec as spec.md, alongside SKILL.md.

Read it on demand, not eagerly. Open spec.md only when you actually need detail beyond what SKILL.md summarizes — e.g. the full base-type / semantic-type hierarchy, the complete list of coercion strategies, or the exact folder-path rules. Do not open it at session start, and do not open it for tasks unrelated to the metadata tree.

If the bundled copy looks out of date with the upstream package, the skill's own README.md documents how to refresh it with extract-spec.