Words API Python API Docs | dltHub

Words API is an English language REST API that returns definitions, related words (synonyms, antonyms, hypernyms, hyponyms, etc.), pronunciations, syllables, frequency, rhymes and search/random word capabilities. The REST API base URL is https://wordsapiv1.p.mashape.com and all requests require a RapidAPI key sent in the X-Mashape-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 Words API data in under 10 minutes.

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What data can I load from Words API?

Here are some of the endpoints you can load from Words API:

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How do I authenticate with the Words API API?

Authentication is via an API key issued through RapidAPI; include the key in every request using the X-Mashape-Key HTTP header and set Accept: application/json.

1. Get your credentials

1. Create or sign in to a RapidAPI account. 2) Subscribe to the Words API (WordsAPI) listing on RapidAPI. 3) From the RapidAPI dashboard select the Words API and copy the provided X-Mashape-Key (RapidAPI key). 4) Use that key as the X-Mashape-Key header in requests.

2. Add them to .dlt/secrets.toml

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[sources.words_api_source]
api_key = "your_rapidapi_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 Words API 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 words_api_pipeline.py

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

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Pipeline words_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset words_api_data
The duckdb destination used duckdb:/words_api.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 words_api_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 words and search from the Words API 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 words_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://wordsapiv1.p.mashape.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "words", "endpoint": {"path": "words/{word}", "data_selector": "results"}},
            {"name": "search", "endpoint": {"path": "words", "data_selector": "results.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="words_api_pipeline",
        destination="duckdb",
        dataset_name="words_api_data",
    )
    load_info = pipeline.run(words_api_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("words_api_pipeline").dataset()
sessions_df = data.words.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM words_api_data.words LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("words_api_pipeline").dataset()
data.words.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 Words API 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 (401)

If you receive 401 Unauthorized: confirm you are sending your RapidAPI key in the X-Mashape-Key header and that the key is active on the RapidAPI dashboard. Also include Accept: application/json.

Not found (404)

A 404 indicates the requested word or resource was not found. Verify the word spelling and URL-encoding (e.g., hyphens, spaces encoded as %20).

Rate limits and RapidAPI subscription errors

Words API access is via RapidAPI. If you hit rate limits or receive subscription/plan errors, check your RapidAPI plan usage and limits in the RapidAPI dashboard. Upgrade or contact RapidAPI/WordsAPI support if you exceed your quota.

Pagination and searching quirks

Search responses use results.data for the array of words and results.total for total matches. Requests may accept limit (1–100) and page parameters; ensure you pass page and limit correctly and encode regex-based query parameters.

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