Monte Carlo Python API Docs | dltHub

Monte Carlo is a data observability platform that exposes a GraphQL API for monitoring, managing, and extracting data from your data environment. The REST API base URL is https://api.getmontecarlo.com/graphql and all requests require either x-mcd-id/x-mcd-token headers or a Bearer token.

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 add "dlt[hub]" and start loading Monte Carlo data in under 10 minutes.

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What data can I load from Monte Carlo?

Here are some of the endpoints you can load from Monte Carlo:

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How do I authenticate with the Monte Carlo API?

Authentication requires either an API key (using 'x-mcd-id' and 'x-mcd-token' headers) or an OAuth 2.0 client credentials flow (using an Authorization: Bearer token).

1. Get your credentials

Log in to the Monte Carlo dashboard, navigate to Settings, and select API Keys (or API Settings). Click Add to generate a new key; select the appropriate type and expiration. Copy the Key ID and Secret immediately, as the secret will not be visible again.

2. Add them to .dlt/secrets.toml

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[sources.monte_carlo_source]
api_key = "your_monte_carlo_api_key_here"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Monte Carlo 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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uv run python monte_carlo_pipeline.py

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

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Pipeline monte_carlo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset monte_carlo_data
The duckdb destination used duckdb:/monte_carlo.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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uv run dlthub 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 graphql and dataExportUrl (or standard graphql endpoint) from the Monte Carlo 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 monte_carlo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getmontecarlo.com/graphql",
            "auth": {"type": "api_key", "api_key": api_key, "name": "x-mcd-token"},
        },
        "resources": [
            {"name": "graphql_resource", "endpoint": {"path": "graphql", "data_selector": "edges"}},
            {"name": "ingest_metadata", "endpoint": {"path": "ingest/v1/metadata", "data_selector": "events"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="monte_carlo_pipeline",
        destination="duckdb",
        dataset_name="monte_carlo_data",
    )
    load_info = pipeline.run(monte_carlo_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("monte_carlo_pipeline").dataset()
sessions_df = data.graphql.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM monte_carlo_data.graphql LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("monte_carlo_pipeline").dataset()
data.graphql.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 Monte Carlo 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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