Load MLS Grid data to DuckDB

MLS Grid is a real estate data-distribution cooperative that provides a normalized RESO Web API for property listing data replication. Everything needed to build a working MLS Grid → 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 MLS Grid 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 MLS Grid 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 MLS Grid 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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MLS Grid API at a glance

These values come from the MLS Grid API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MLS Grid API?

All requests must include an Authorization header with a Bearer token. Additionally, every request must include an Accept-Encoding: gzip header, or it will return an HTTP 400 error.

1. Get your credentials

To obtain MLS Grid API credentials, you must follow the formal subscription and licensing process as there is no self-serve public sign-up: 1. Sign the MLS Grid Master Data License Agreement via the MLS Grid online portal. 2. Ensure a data subscription is created and a licensee is added. 3. Obtain approval from the originating Multiple Listing Service (MLS). 4. Once approved and payment is processed, log in to the MLS Grid web application. 5. Navigate to your subscription details page and locate the Token tab, where your long-lived OAuth 2.0 bearer access token will be generated and displayed.

2. Add them to .dlt/secrets.toml

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[sources.mls_grid_source]
api_key = "your_bearer_token_here"

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 MLS Grid data can I load into DuckDB?

These are the MLS Grid endpoints dlt can load into DuckDB:

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How do I load only new MLS Grid records?

MLS Grid exposes ModificationTimestamp on Property, 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": "property", "endpoint": {
    "path": "Property",
    "data_selector": "value",
    "incremental": {"cursor_path": "ModificationTimestamp", "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 MLS Grid pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading Property and Lookup from the MLS Grid API into DuckDB:

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

@dlt.source
def mls_grid_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mlsgrid.com/v2/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "property", "endpoint": {"path": "Property", "data_selector": "value"}},
            {"name": "member", "endpoint": {"path": "Member", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mls_grid_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mls_grid_pipeline",
        destination="duckdb",
        dataset_name="mls_grid_data",
    )
    load_info = pipeline.run(mls_grid_source())
    print(load_info)


if __name__ == "__main__":
    load_mls_grid_to_duckdb()

Run it with python mls_grid_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 MLS Grid 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("mls_grid_pipeline").dataset()
df = data.property.df()
print(df.head())

SQL:

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SELECT * FROM mls_grid_data.property LIMIT 10;

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

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How do I deploy the MLS Grid 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 MLS Grid 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 MLS Grid 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