<br />

Use Gemini inFirebaseto help craft schemas, queries, and mutations for your client-side code.

Describe your app, data model, or a desired query or mutation in natural language, and Gemini inFirebasegenerates theData Connectequivalent.

This AI assistance is available in the following development contexts:

- In theFirebaseconsole, you can generate, test, and deploy your schemas and operations.
- In your local environment, you can use the Firebase CLI and Data Connect VS Code extension to generate, test, and develop on your app with the emulator.
- AI-powered development tools can use Firebase MCP server to generate, test, develop your app.

Learn more aboutData Connect[schema](https://firebase.google.com/docs/data-connect/schemas-guide),[query](https://firebase.google.com/docs/data-connect/queries-guide)and[mutation](https://firebase.google.com/docs/data-connect/mutations-guide)syntax in guides.

## HowAI assistance forData Connectuses your data

For more information, see[How Gemini inFirebaseuses your data](https://firebase.google.com/docs/gemini-in-firebase#how-gemini-in-firebase-uses-your-data).

## Set upAI assistance forData Connect

To use AI assistance withData Connect, enable Gemini inFirebaseas described in[Set up Gemini inFirebase](https://firebase.google.com/docs/ai-assistance/gemini-in-firebase/set-up-gemini).
| **Important:** Project members must have theFirebase Data ConnectAPI Viewer role (`roles/firebasedataconnect.viewer`) to generateData Connectoperations with Gemini inFirebase. You will needFirebase Data ConnectAPI Data Admin role (`roles/firebasedataconnect.dataAdmin`) to run generated queries inFirebaseconsole.

## Generate schema, queries, and mutations with Gemini inFirebase

You can use AI assistance forData Connectin many of your workflows.
| **Important:** AI assistance forData Connectis an early-stage technology that can sometimes generate output that seems plausible but is, in fact, incorrect. It may respond with inaccurate information that doesn't represent Google's views. Validate all output from Gemini before you use it and do not use untested generated code in production. Do not enter personally-identifiable information (PII) or user data into a**Help me write GraphQL** prompt. For more information, see[HowAI assistance forData Connectuses your data](https://firebase.google.com/docs/data-connect/ai-assistance#governance)and[Gemini in Google Cloud and responsible AI](https://cloud.google.com/duet-ai/docs/discover/responsible-ai).

### In theFirebaseconsole

When you create aData Connectservice, theFirebaseconsole offers a "Getting started with Gemini" experience.

You can describe an app idea, and AI assistance generates the following:

- A complete schema based on your app idea.
- Example operations and data mutations.

From the data page, you can use the**Help me write GraphQL** pen_sparkbutton to generate and execute operations based on natural language. Check out[some example use cases](https://firebase.google.com/docs/data-connect/ai-assistance#use-cases).

This workflow is described in our[Get started guide](https://firebase.google.com/docs/data-connect/quickstart). You can continue in your local development environment with the deployed schema and operations.

### In your local environment

You can also get AI assistance from the Firebase CLI and the Data Connect VS Code extension.

You can provide your app idea to`firebase init dataconnect`, and it generates the following:

- A complete schema based on your app idea.
- Example operations and a seed data mutation.

The Data Connect VS Code extension provides the following features:

- **Generates/Refine Operations Code Lens** to convert GraphQL comments intoData Connectoperations.
- Seamless integration withGemini Code Assistand Firebase MCP server.

This workflow is described in our[Get started guide for local prototyping](https://firebase.google.com/docs/data-connect/quickstart-local).

### Use the Firebase MCP server with AI-powered development tools

The Firebase MCP server works with any AI assistant tools that can act as an MCP client, including Gemini CLI,Gemini Code Assist, Cursor, Visual Studio Code Copilot, Claude Desktop, and Windsurf Editor.

The Firebase MCP server provides additional context and capabilities to help AI-powered development tools work better withData Connect. It can do the following:

- Set up new project directories and generated SDKs.
- Build and iterate on schemas, operations based on compile errors.
- Generate new schemas, operations based on requirements.
- Generate and execute operations against local emulator or backend services.
- Gather information on existing services.

To use Firebase MCP server:

1. Set up your MCP client[following this guide](https://firebase.google.com/docs/cli/mcp-server#setup).
2. Ask for help related toData Connect. Examples prompts:
   1. "Set up aData Connectproject for a pizza delivery app."
   2. "FixData Connectcompile errors."
   3. "In the home page, I need to show active chat rooms and friend list. Generate aData Connectquery."
   4. "What users are in my localData Connectemulator?"
   5. "What Google Cloud regions are myData Connectservices in?"

## Example use cases for generating operations

The following sections describe sample use cases:

- [Return the top five movies in descending order by rating](https://firebase.google.com/docs/data-connect/ai-assistance#create-top-five-movie)
- [Create a mutation that adds a movie to the database based on user input](https://firebase.google.com/docs/data-connect/ai-assistance#create-a-mutation)
- [Create a query that lists reviews based on user-provided genre and ratings](https://firebase.google.com/docs/data-connect/ai-assistance#create-a-query)

### Return the top five movies in descending order by rating

To useAI assistance forData Connectto generate GraphQL based on natural language:

1. Open[Data Connect](https://console.firebase.google.com/project/_/dataconnect)in your project and, under**Services**, select your data source.

2. Click**Data**.

3. Click the**Help me write GraphQL** pen_sparkicon. Describe in natural language the[query](https://firebase.google.com/docs/data-connect/queries-guide)or[mutation](https://firebase.google.com/docs/data-connect/mutations-guide)you want to generate, and click**Generate**.

   For example, if you're using the Movies data source referenced in the["Build withData Connect(web)" codelab](https://firebase.google.com/codelabs/firebase-dataconnect-web), you could ask, "**Return the top five movies of 2022, in descending order by rating**," which might return a result like the following:  

       query TopMovies2022 {
         movies(where: {releaseYear: {eq: 2022}}, orderBy: [{rating: DESC}], limit: 5) {
           id
           title
           rating
           releaseYear
         }
       }

4. Review the response:

   - If the response looks correct, click**Insert**to insert the response into the code editor.
   - If the response could be refined, click**Edit** , update the prompt, and click**Regenerate**.
5. After you've accepted the response, set the following in the**Parameters**section, if applicable:

   - **Variables** : If your query or mutation contains variables, define them here. Use JSON to define them, for example,`{"title":"The
     Matrix", "releaseYear":"1999"}`.
   - **Authorization** : Choose the[Authorization context](https://firebase.google.com/docs/data-connect/authorization-and-security)(Administrator, Authenticated, or Unauthenticated) with which to run the query or mutation.

   | **Warning:** If you're running a[mutation](https://firebase.google.com/docs/data-connect/schemas-queries-mutations#mutations-movie), review it carefully before running it. Running a mutation in the data editor will execute the request.
6. Click**Run**in the code editor and review results.

To test multiple queries or mutations in the code editor, ensure they are named. For example, the following query is named`GetMovie`. Move your cursor into the first line of the query or mutation to activate the**Run**button.  

    query GetMovie($myKey: Movie_Key!) {
      movie(key: $myKey) { title }
    }

| **Note:** If you've just created aData Connectproject and requested a new PostgreSQL instance,Data Connectwill create a temporary database for you to use while your permanent PostgreSQL database is being provisioned, then migrate all data when provisioning is done. This temporary database is great for exploring your schema and CRUD operations. The temporary database is not a PostgreSQL database. It has[some limitations](https://firebase.google.com/docs/data-connect/manage-services-and-databases#ephemeral-limitations).

### Create a mutation that adds a movie to the database based on user input

This example shows how to use natural language to generate a GraphQL mutation that populates your database. This example assumes that you're using the movie database schema used in the[Firebase Data Connectdocumentation](https://firebase.google.com/docs/data-connect)and["Build withData Connect(web)" codelab](https://firebase.google.com/codelabs/firebase-dataconnect-web).
| **Warning:** Running a mutation in the data editor will execute the mutation and may change your data.

1. From the[Firebaseconsole](https://console.firebase.google.com/project/_/dataconnect), open**Data Connect**.

2. Select your service and data source, then open the**Data**tab.

3. Click the**Help me write GraphQL** pen_sparkicon and describe your mutation:

       Create a movie based on user input.

4. Click**Generate**. The mutation is returned. For example, Gemini might return a mutation like:

       mutation CreateMovie($title: String!, $releaseYear: Int!, $genre: String!, $rating: Float!, $description: String!, $imageUrl: String!, $tags: [String!] = []) @auth(level: USER) {
         movie_insert(data: {
           title: $title,
           releaseYear: $releaseYear,
           genre: $genre,
           rating: $rating,
           description: $description,
           imageUrl: $imageUrl,
           tags: $tags
         })
       }

5. Review the output. If needed, click**Edit** to refine the prompt and click**Regenerate**.

6. Next, click**Insert**to insert the mutation into the data editor.

7. To execute the mutation, you'll need to add variables. From the**Parameters** section, open**Variables**and include some test variables:

       {"title":"My amazing movie", "releaseYear":2024, "genre": "Comedy",
       "rating": 8, "description": "A new movie to test mutations",
       "imageUrl": "", "tags": ["comedy","space travel"]}

8. Click**Run**.

   | **Tip:** You can also ask Gemini to expand the output. For example, to include comments and your source prompt in the output, you might use "Create a movie based on user input. Be sure to document the mutation and include this prompt in your response".
9. Next, create a query that verifies that your movie was added. Click the**Help me write GraphQL** pen_sparkand, in the box that appears, type your prompt:

       List all movies from 2024 that include all of these tags: 'space travel', 'comedy'.

   Gemini might return a response like the following:  

       query ComedySpaceTravelMovies2024 @auth(level: PUBLIC) {
         movies(
           where: {
           releaseYear: { eq: 2024 },
           tags: { includesAll: ["space travel", "comedy"] }
           }
         ) {
             id
             title
             imageUrl
             releaseYear
             genre
             rating
             description
             tags
           }
       }

10. Insert and run the query. The movie you added should appear in the**History**field.

| **Note:** If you've just created aData Connectproject and requested a new PostgreSQL instance,Data Connectwill create a temporary database for you to use while your permanent PostgreSQL database is being provisioned, then migrate all data when provisioning is done. This temporary database is great for exploring your schema and CRUD operations. The temporary database is not a PostgreSQL database. It has[some limitations](https://firebase.google.com/docs/data-connect/manage-services-and-databases#ephemeral-limitations).

### Create a query that lists reviews based on user-provided genre and ratings

This example shows how to use natural language to generate a GraphQL query. This example assumes that you're using the movie database used in the[Firebase Data Connectdocumentation](https://firebase.google.com/docs/data-connect)and["Build withData Connect(web)" codelab](https://firebase.google.com/codelabs/firebase-dataconnect-web).

1. From the[Firebaseconsole](https://console.firebase.google.com/project/_/dataconnect), open**Data Connect**.

2. Select your service and data source, then open the**Data**tab.

3. Click the**Help me write GraphQL** pen_sparkicon and describe your query:

       List all movie reviews, based on user-configurable genre and ratings.

4. Click**Generate**. The query is returned. For example, Gemini might return a query like:

       query ListReviewsByGenreAndRating($genre: String, $minRating: Int, $maxRating: Int) @auth(level: PUBLIC) {
         reviews(where: {
           movie: {
             genre: {eq: $genre}
           },
           rating: {ge: $minRating, le: $maxRating}
         }) {
           id
           user {
             username
           }
           movie {
             title
             genre
           }
           rating
           reviewText
           reviewDate
         }
       }

5. Review the output. If needed, click**Edit** to refine the prompt and click**Regenerate**.

6. Next, click**Insert**to insert the mutation into the data editor.

7. To test this query, you'll need to add variables. From the**Parameters** section, open**Variables**and include variables to use for testing:

       {"genre":"sci-fi", "minRating":4, "maxRating":9}

8. Click**Run**.

| **Tip:** You can also ask Gemini to expand the output. For example, to include comments and your source prompt in the output, you might use "List all movie reviews, based on user-configurable genre and ratings. Be sure to document the query and include this prompt in your response."
| **Note:** If you've just created aData Connectproject and requested a new PostgreSQL instance,Data Connectwill create a temporary database for you to use while your permanent PostgreSQL database is being provisioned, then migrate all data when provisioning is done. This temporary database is great for exploring your schema and CRUD operations. The temporary database is not a PostgreSQL database. It has[some limitations](https://firebase.google.com/docs/data-connect/manage-services-and-databases#ephemeral-limitations).

## Design prompts for third-party AI assistance tools

As with all AI assistance tools, better prompts yield more useful outputs.

When you provide natural language prompts to Gemini inFirebase, behind the scenes, the assistant translates your inputs to a more fully-developed prompt.

If you're working with third-party AI tools like Cursor or Windsurf, you can get betterData Connectrecommendations by using similar, more detailed prompts.

We've published prompt templates for you to download, adapt, and copy into your IDE:

- A template prompt for[schema generation](https://raw.githubusercontent.com/firebase/firebase-tools/refs/heads/master/templates/dataconnect-prompts/schema-generation-cursor-windsurf-rule.txt)
- A template prompt for[operation generation](https://raw.githubusercontent.com/firebase/firebase-tools/refs/heads/master/templates/dataconnect-prompts/operation-generation-cursor-windsurf-rule.txt)

After downloading and modifying, create a prompt in familiar tooling (for example Cursor or Windsurf) as follows:

- In Cursor (be sure to review the latest[instructions from Cursor](https://docs.cursor.com/context/rules#creating-a-rule)):

  1. Click the settings icon on top right.
  2. Select the**Rules**tab.
  3. Under the**Project Rules** , click**Add a new rule**button.
  4. Copy and paste the rule.
- In Windsurf (be sure to review the latest[instructions from Windsurf](https://docs.windsurf.com/windsurf/cascade/memories#rules)):

  1. Open the Cascade window by clicking the**Cascade**button in the top right corner.
  2. Click the**Customizations** icon in the top right slider menu in Cascade, then navigate to the**Rules**panel.
  3. Click the**+ Global** or**+ Workspace**button to create new rules at either the global or workspace level, respectively.
  4. Copy and paste the rule.

## TroubleshootAI assistance forData Connect

See[Troubleshoot Gemini inFirebase](https://firebase.google.com/docs/gemini-in-firebase/set-up-gemini#troubleshoot-gemini-in-firebase).

## Pricing

AI assistance forData Connectis available as part of Gemini inFirebaseand is included for individual users.

See[Gemini inFirebasepricing](https://firebase.google.com/docs/gemini-in-firebase#pricing)for more information.

## Next steps

- Learn more about[schema](https://firebase.google.com/docs/data-connect/schemas-guide),[query](https://firebase.google.com/docs/data-connect/queries-guide)and[mutation](https://firebase.google.com/docs/data-connect/mutations-guide).
- Learn more about[Gemini inFirebase](https://firebase.google.com/docs/gemini-in-firebase).