DCC-EX Python API Docs | dltHub

DCC-EX is a native command protocol and API for the EX-CommandStation that allows control and monitoring of model railway devices (locos, turnouts, sensors, accessories) via serial, WiFi or Ethernet using framed angle-bracket commands. The REST API base URL is `` and no authentication; access is by direct serial or local network connectivity to the EX-CommandStation (no bearer/API key documented).

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading DCC-EX data in under 10 minutes.

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What data can I load from DCC-EX?

Here are some of the endpoints you can load from DCC-EX:

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How do I authenticate with the DCC-EX API?

No auth headers or tokens are documented; clients connect via serial or local network to the command station and send/receive angle-bracket framed commands.

1. Get your credentials

Not applicable — DCC-EX does not provide API credentials in documentation; access is via physical serial port or network connection to your EX-CommandStation device.

2. Add them to .dlt/secrets.toml

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[sources.dcc_ex_source]

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the DCC-EX API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python dcc_ex_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline dcc_ex_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dcc_ex_data
The duckdb destination used duckdb:/dcc_ex.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline dcc_ex_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads turnouts and locos from the DCC-EX API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def dcc_ex_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            {"name": "turnouts", "endpoint": {"path": "JT"}},
            {"name": "locos", "endpoint": {"path": "JL"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dcc_ex_pipeline",
        destination="duckdb",
        dataset_name="dcc_ex_data",
    )
    load_info = pipeline.run(dcc_ex_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("dcc_ex_pipeline").dataset()
sessions_df = data.turnouts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dcc_ex_data.turnouts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dcc_ex_pipeline").dataset()
data.turnouts.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load DCC-EX data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Invalid command errors

The device returns a minimal error response of for invalid commands. Ensure commands follow the documented <OPCODE param1 param2 ...> format (no nested <).

Concurrency and broadcast responses

Multiple clients may be connected; responses are broadcast and there is no client-specific correlation. Do not assume a response received immediately after sending a command belongs to that command; parse OPCODE and parameters to match.

No HTTP/JSON; parsing and framing

Responses are not JSON. Parse incoming streams for messages beginning with '<' and ending with '>' and then tokenise the OPCODE and parameters by spaces. Ignore unknown broadcasts.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack