RST Cloud Python API Docs | dltHub

RST Cloud API is a REST API providing threat intelligence services including IOC lookup, threat feed snapshots, WHOIS, scan tools (SSL, favicon, Cobalt Strike), report hub and noise control. The REST API base URL is https://api.rstcloud.net/v1 and All requests require an API key sent in the x-api-key header..

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 RST Cloud data in under 10 minutes.

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What data can I load from RST Cloud?

Here are some of the endpoints you can load from RST Cloud:

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How do I authenticate with the RST Cloud API?

Authentication uses an apiKey provided by RST Cloud. Include the API key in the request header 'x-api-key'. The /auth/check endpoint can be used to validate the key (returns 200 when valid).

1. Get your credentials

1. Request or generate an API token via the RST Cloud website/free trial or contact trial@rstcloud.net or your account representative. 2) Receive API key (token). 3) Optionally set environment variable RST_API_KEY for client libraries or store the key in your application's secrets. 4) Use the key by adding header 'x-api-key: YOUR_KEY' to API requests.

2. Add them to .dlt/secrets.toml

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[sources.rst_cloud_source]
api_key = "your_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 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 RST Cloud 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 rst_cloud_pipeline.py

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

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Pipeline rst_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset rst_cloud_data
The duckdb destination used duckdb:/rst_cloud.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 rst_cloud_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 ioc and reports from the RST Cloud 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 rst_cloud_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.rstcloud.net/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "ioc_lookup", "endpoint": {"path": "ioc", "data_selector": "(top-level object) — response returns IOC fields directly (not an array). For list-style queries this endpoint returns single object or objects with keys like ioc_value, ioc_type, id, tags, threat, collect, fseen, lseen, title, score, description."}},
            {"name": "reports", "endpoint": {"path": "reports", "data_selector": "reports"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rst_cloud_pipeline",
        destination="duckdb",
        dataset_name="rst_cloud_data",
    )
    load_info = pipeline.run(rst_cloud_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("rst_cloud_pipeline").dataset()
sessions_df = data.ioc_lookup.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM rst_cloud_data.ioc_lookup LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("rst_cloud_pipeline").dataset()
data.ioc_lookup.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 RST Cloud 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

Authentication failures

If you receive 401/403 or 503 with message 'Forbidden' or 'access denied', verify the x-api-key header contains a valid API key. Use GET /auth/check to validate the key (200 = valid).

Feed snapshot redirects and downloads

Feed endpoints (/ip, /domain, /url, /hash) return 302 with a Location header pointing to a presigned URL for the gzipped snapshot. Follow redirects or extract Location to download the gzip file.

IOC lookup responses and missing fields

GET /ioc returns IOC records as an object containing fields like ioc_value, ioc_type, id, title, score, tags, threat, collect, fseen, lseen. Some fields may be present but empty depending on IOC type.

Common error responses

RST Cloud uses structured error schemas: RST400 (400 Bad Request with {"error": "..."}), RST500 (server error with {"error": "..."}), RST503 (access denied with {"message": "Forbidden"}), RSTIOC400 for IOC-specific bad requests. Check response body for 'error' or 'message' keys.

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