Jira Service Desk Python API Docs | dltHub

Jira Service Desk REST API provides endpoints for managing schedules, request types, and customer requests. It uses pagination and resource expansion for efficient data handling. Essential permissions and authentication methods are required for accessing these endpoints. The REST API base URL is https://<your-site>.atlassian.net/rest/servicedeskapi and Basic auth (Atlassian account email + API token) for scripts; OAuth 2.0 (3LO) supported for apps..

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 Jira Service Desk data in under 10 minutes.

---

What data can I load from Jira Service Desk?

Here are some of the endpoints you can load from Jira Service Desk:

---

How do I authenticate with the Jira Service Desk API?

For personal scripts and bots use HTTP Basic auth with header Authorization: Basic <base64(email:api_token)>. For integrations/apps use OAuth 2.0 (3LO) with requests to api.atlassian.com and Authorization: Bearer <access_token>. Required special headers: X-Atlassian-Token: no-check for multipart uploads; X-ExperimentalApi: opt-in for experimental endpoints.

1. Get your credentials

1. Sign in to your Atlassian account at id.atlassian.com. 2. Open "Manage API tokens" (https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/). 3. Create a new API token, copy it (it will be used as the password in basic auth together with your Atlassian account email). 4. For OAuth 2.0 (3LO) create an OAuth app in your developer console and perform the authorization code flow to obtain an access token.

2. Add them to .dlt/secrets.toml

Copy
[sources.jira_service_desk_source]
email = "your_atlassian_email@example.com"
api_token = "your_api_token_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Jira Service Desk 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:

Copy
python jira_service_desk_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline jira_service_desk_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.

---

Python pipeline example

This example loads servicedesk and request from the Jira Service Desk API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def jira_service_desk_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-site>.atlassian.net/rest/servicedeskapi",
            "auth": {
                "type": "http_basic",
                "password": api_token,
            },
        },
        "resources": [
            {"name": "servicedesk", "endpoint": {"path": "servicedesk", "data_selector": "values"}},
            {"name": "request", "endpoint": {"path": "request", "data_selector": "values"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jira_service_desk_pipeline",
        destination="duckdb",
        dataset_name="jira_service_desk_data",
    )
    load_info = pipeline.run(jira_service_desk_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.

---

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):

Copy
import dlt

data = dlt.pipeline("jira_service_desk_pipeline").dataset()
sessions_df = data.request.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM jira_service_desk_data.request LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("jira_service_desk_pipeline").dataset()
data.request.df().head()

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

---

What destinations can I load Jira Service Desk 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.

---

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.

Copy
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