<option value="$PROJECT_DIR$/platforms/core-configuration/kotlin-dsl-tooling-builders" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/kotlin-dsl-tooling-model-impls" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/kotlin-dsl-tooling-models" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-core" />

      {
        "subprojects": [
          "declarative-dsl-provider",
          "declarative-dsl-tooling-builders",
          "enterprise",
          "execution-e2e-tests",
          "file-collections",
          "ide-plugins",
          "integ-test",
          "ivy",
          "jacoco",
          "kotlin-dsl-tooling-builders",
          "language-groovy",
          "maven",
          "platform-native",

  {
    "name": "tooling-api-provider",
    "path": "platforms/core-runtime/tooling-api-provider",
    "unitTests": false,
    "functionalTests": false,
    "crossVersionTests": false
  },
  {
    "name": "tooling-native",
    "path": "platforms/native/tooling-native",
    "unitTests": false,
    "functionalTests": false,
    "crossVersionTests": true
  },
  {
    "name": "tooling-native-model-impls",

  * This starts the [Gradle Launcher](https://blog.gradle.org/how-gradle-works-1#local-gradle-distribution-in-cli), which itself requires a JVM
* The [Gradle Tooling API client](https://docs.gradle.org/8.8/userguide/third_party_integration.html#embedding)
  * This requires a Java application, and thus runtime, to run the tooling API client

As indicated, each of those components require a Java runtime to run.

4. The daemon runs the request, sending back data such as logging output or tooling API events and intermediate models while doing so.
5. The daemon sends the result back. For some requests, this might be a simple success/failure result, and for others this might also include a more complex object, such as a tooling API model.

  stars: 3501
  owner_login: IBM
  owner_html_url: https://github.com/IBM
- name: opyrator
  html_url: https://github.com/ml-tooling/opyrator
  stars: 3137
  owner_login: ml-tooling
  owner_html_url: https://github.com/ml-tooling
- name: docarray
  html_url: https://github.com/docarray/docarray
  stars: 3120
  owner_login: docarray
  owner_html_url: https://github.com/docarray

- CLI client. This is the `gradle` or `gradlew` command, and is responsible for locating, starting and interacting with the Gradle daemon, potentially downloading the Gradle distribution. 
- Tooling API client. This a library that is embedded into applications, such as IDEs or CI agents, that allows them to act as a Gradle client.

platforms/core-configuration/kotlin-dsl-plugins/            @gradle/bt-tide
platforms/core-configuration/kotlin-dsl-provider-plugins/   @gradle/bt-tide
platforms/core-configuration/kotlin-dsl-tooling-builders/   @gradle/bt-tide
platforms/core-configuration/kotlin-dsl-tooling-models/     @gradle/bt-tide

# Core automation platform (Declarative DSL)
platforms/core-configuration/declarative-dsl-api/                   @gradle/bt-declarative-gradle-project

### Build execution model

Gradle generally does some work in response to a client request. There are several different clients, for example the `gradlew` command or the tooling API client, 
that can send requests to a Gradle daemon.
Each daemon runs one request at a time. Generally speaking, the daemons only act in response to a request from a client and do not take any action on their own.

{* ../../docs_src/response_model/tutorial003_01_py310.py hl[7:10,13:14,18] *}

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 { #type-annotations-and-tooling }

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