Load MusicBrainz data to DuckDB

MusicBrainz is an open-source, community-maintained music metadata service that provides a REST API to query and submit information about entities like artists, releases, and recordings. Everything needed to build a working MusicBrainz → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your MusicBrainz to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from MusicBrainz to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the MusicBrainz API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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MusicBrainz API at a glance

These values come from the MusicBrainz API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MusicBrainz API?

The service supports OAuth2 or HTTP Digest authentication for POST requests and user-specific data. Every request must include a custom, meaningful 'User-Agent' header to identify the application and contact information.

1. Get your credentials

MusicBrainz does not use traditional API keys for read access. For public, non-commercial read access, you only need to provide a meaningful User-Agent header in your requests. For data submission or access to private user information, you must use OAuth2. To obtain OAuth2 credentials, register your application on the MusicBrainz website via your user account profile settings, which will provide you with a Client ID and Client Secret.

2. Add them to .dlt/secrets.toml

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[sources.musicbrainz_source]
client_id = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What MusicBrainz data can I load into DuckDB?

These are the MusicBrainz endpoints dlt can load into DuckDB:

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How do I load only new MusicBrainz records?

MusicBrainz exposes offset on /<RESULT_ENTITY_TYPE>?<BROWSING_ENTITY_TYPE>=<MBID>, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "browse_entities", "endpoint": {
    "path": "/<RESULT_ENTITY_TYPE>?<BROWSING_ENTITY_TYPE>=<MBID>",
    "incremental": {"cursor_path": "offset", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated MusicBrainz pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading lookup and browse from the MusicBrainz API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def musicbrainz_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://musicbrainz.org/ws/2",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "browse_entities", "endpoint": {"path": "/<RESULT_ENTITY_TYPE>?<BROWSING_ENTITY_TYPE>=<MBID>"}},
            {"name": "search_entities", "endpoint": {"path": "/<ENTITY_TYPE>?query=<QUERY>"}}
        ],
    }
    yield from rest_api_resources(config)


def load_musicbrainz_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="musicbrainz_pipeline",
        destination="duckdb",
        dataset_name="musicbrainz_data",
    )
    load_info = pipeline.run(musicbrainz_source())
    print(load_info)


if __name__ == "__main__":
    load_musicbrainz_to_duckdb()

Run it with python musicbrainz_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query MusicBrainz data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("musicbrainz_pipeline").dataset()
df = data.browse_entities.df()
print(df.head())

SQL:

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SELECT * FROM musicbrainz_data.browse_entities LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the MusicBrainz to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw MusicBrainz loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load MusicBrainz data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack