> For the complete documentation index, see [llms.txt](https://docs.pyck.cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pyck.cloud/graphql-api/types/datatype.md).

# DataType

DataType Type

Represents a data type definition in the system, which defines the structure of dynamic data stored in the `data` field of various entities.

## Fields

| Field            | Type      | Description                                                                   |
| ---------------- | --------- | ----------------------------------------------------------------------------- |
| `id`             | `ID!`     | Unique identifier of the data type                                            |
| `name`           | `String`  | Human-readable name of the data type                                          |
| `slug`           | `String`  | Machine-readable slug used to reference the data type                         |
| `description`    | `String`  | Description of the data type and its purpose                                  |
| `jsonSchema`     | `String!` | JSON Schema definition (as a string) that describes the structure of the data |
| `frontendSchema` | `String`  | Optional frontend-specific schema information (e.g., UI form definitions)     |
| `default`        | `Boolean` | Whether this is the default data type for its entity                          |
| `entity`         | `String!` | The entity type this data type is associated with (e.g., "customer", "item")  |

## Interfaces

* `NodeDataType`

## Usage Example

```graphql
query {
  dataTypes(
    where: { entity: "item" }
  ) {
    edges {
      node {
        id
        name
        slug
        description
        jsonSchema
        frontendSchema
        default
        entity
      }
    }
  }
}
```

```graphql
mutation {
  createDataType(
    input: {
      name: "Premium Product",
      slug: "premium_product",
      description: "Schema for premium products with extended attributes",
      entity: "item",
      jsonSchema: "{\"type\":\"object\",\"properties\":{\"name\":{\"type\":\"string\"},\"description\":{\"type\":\"string\"},\"premium_features\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}},\"warranty_period\":{\"type\":\"integer\"},\"warranty_unit\":{\"type\":\"string\",\"enum\":[\"days\",\"months\",\"years\"]}},\"required\":[\"name\",\"premium_features\",\"warranty_period\",\"warranty_unit\"]}"
    }
  ) {
    id
    name
    slug
    jsonSchema
  }
}
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.pyck.cloud/graphql-api/types/datatype.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.