```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

    $ cd docs/en/
    ```

    Then run `mkdocs` in that directory:

    ```console
    $ mkdocs serve --dev-addr 8008
    ```

#### Typer CLI (optional)

The instructions here show you how to use the script at `./scripts/docs.py` with the `python` program directly.

But I'll show you how to improve that next. 🤓

## Custom Operation IDs and Better Method Names

You can **modify** the way these operation IDs are **generated** to make them simpler and have **simpler method names** in the clients.

#### Check the `openapi.json`

If you are curious about how the raw OpenAPI schema looks like, FastAPI automatically generates a JSON (schema) with the descriptions of all your API.

You can see it directly at: <a href="http://127.0.0.1:8000/openapi.json" class="external-link" target="_blank">http://127.0.0.1:8000/openapi.json</a>.

It will show a JSON starting with something like:

```JSON
{
    "openapi": "3.1.0",

```Python
{!> ../../../docs_src/app_testing/app_b/test_main.py!}
```

Whenever you need the client to pass information in the request and you don't know how to, you can search (Google) how to do it in `httpx`, or even how to do it with `requests`, as HTTPX's design is based on Requests' design.

Then you just do the same in your tests.

E.g.:

# Benchmarks

Independent TechEmpower benchmarks show **FastAPI** applications running under Uvicorn as <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">one of the fastest Python frameworks available</a>, only below Starlette and Uvicorn themselves (used internally by FastAPI).

          name: coverage
          path: coverage
      - run: pip install coverage[toml]
      - run: ls -la coverage
      - run: coverage combine coverage
      - run: coverage report
      - run: coverage html --show-contexts --title "Coverage for ${{ github.sha }}"
      - name: Store coverage HTML
        uses: actions/upload-artifact@v3
        with:
          name: coverage-html
          path: htmlcov

Routes are declared in a single place, using functions declared in other places (instead of using decorators that can be placed right on top of the function that handles the endpoint). This is closer to how Django does it than to how Flask (and Starlette) does it. It separates in the code things that are relatively tightly coupled.

!!! check "Inspired **FastAPI** to"

```

## Other middlewares

You can later read more about other middlewares in the [Advanced User Guide: Advanced Middleware](../advanced/middleware.md){.internal-link target=_blank}.

    ```

With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.

How does this work? Let's check that out. 🤓

### Type Annotations and Tooling

First let's see how editors, mypy and other tools would see this.