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Data Connect: Full Text Search, Enums, and More in Firebase: Web Architecture & Implementation Guide

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Alex Patterson
Alex Patterson

If you have ever watched an AI coding agent hallucinate deprecated Firebase v8 syntax or deploy wide-open Firestore rules, you know that raw LLM prompts are not enough for production backends. Following the announcement of Data Connect: Full Text Search, Enums, and More by Andrea Wu on the Official Firebase Blog (2025-07-23), here is our hands-on CodingCat.dev breakdown of what actually shipped, how it works under the hood, and how we wire it into production TypeScript, Next.js, Astro, SvelteKit, React, and Angular applications. Note: Firebase Data Connect has been renamed to SQL Connect. Learn more in the SQL Connect announcement.

The Mental Model: Why Data Connect: Full Text Search, Enums, and More Matters for Web Devs

As a Google Developer Expert (GDE) for Firebase and Senior Solutions Engineer at CodingCat.dev, I evaluate every Firebase update through one lens: does it reduce boilerplate while keeping our runtime security and cloud bills predictable? Instead of relying on stale training weights, wiring up the Model Context Protocol (MCP) and structured Firebase agent skills gives your IDE or CLI direct, schema-aware tools to provision resources, inspect security rules, and run deterministic CLI deployments.

AJ Pro-Tip from Alex: Never let an autonomous coding agent push Firestore or Data Connect rules straight to production without a gate. Always require the agent to run your test suite against the Firebase Local Emulator Suite first, and audit every generated rule for strict request.auth.uid ownership checks.

How It Works: Technical Breakdown & Implementation

  • Full-text search: efficiently locate information within large datasets by searching for keywords and phrases across multiple columns at once.
  • Enum support: quickly define a list of static, predefined values with a specific order
  • SDK download: run a script from the console to set up the SDK for you
  • MCP support: use the Firebase MCP server to use Data Connect for working with data, retrieving schemas. Some of the tools use Gemini in Firebase to help you generate schemas and operations as well.

Read more below for more information about each one!

Full-text search

Data Connect now supports full-text search, powered by Postgres. Full-text search lets you quickly and efficiently locate information within large datasets by searching for keywords and phrases across multiple columns at once.

To add full-text search to your service, add the @searchable directive to String fields in your schema that you want to search over. For example:

type
  @table

  # The fields we want to search over
  title
  genre
  description

  # Some other fields that we won't search over
  rating
  imageUrl
  releaseYear
}

You can then perform full-text search across all of those fields by adding the <pluralType>_search field to a query. In this case, it’ll be movies_search:

# Find movies with searchTerm in their title, genre, or description
query
  movies_search(query: $searchTerm) {
    id
    title
    imageUrl
    releaseYear
    genre
    rating
    description
  }
}

You can imagine if we search on our entire dataset, there may be information we don’t actually want. To help with this, there are some ways to fine-tune search results:

  • Filters, limits, orders, and more: many of the same arguments used for list fields <pluralType> can be used to control your search results, such as order, where, limit, offset, and distinct.
  • Language choice: by default, documents get parsed in English. However, Data Connect supports nineteen languages out of the box. You can choose the right language, which improves accuracy and relevance of search results.
  • Query format: by default, full-text search uses web semantics for queries, similar to Google search. You can change this behavior if you want other query formats, such as matching all words or an exact phrase.
  • Relevance threshold: if you feel that some search results are irrelevant, you can set the relevance threshold to only receive results above the threshold

To add full-text search to your app today, check out the documentation.

Enum support

Data Connect now supports enums, powered by Postgres, and they let you quickly define a list of static, predefined values with a specific order.

To add an enum to your service, you first define the enum type along with its pre-defined values, and then you reference it in your table. In terms of using enums in your client app, they work just like types, queries, and mutations: you define these in GraphQL, and Data Connect automatically generates SDKs. The SDKs include your enums so they can be used in the generated types and operations.

enum
  ENGLISH
  FRENCH
  SPANISH
  GERMAN
  JAPANESE
  "
  OTHER_LANGUAGES
}

type
  @table
  title
  genre
  description
  originalLanguage
  availableLanguages
  // other fields
}

You can also use the following features for enums:

  • Querying: use enums in your queries
  • Filtering: enums have the standard filtering actions available - eq, ne, ge, gt, lt, le, in, and nin, as well as includes, excludes, includesAll, and excludesAll for lists.
  • Use in aggregates: _count is supported for enums.
  • Add or Remove supported enum values: You can add new values to your enum. Because the order of the enum list is very meaningful, insert your new values wisely, and never reorder the values as doing so changes how filtering is applied.
  • Emulator support: Using the VS Code local emulator, you can test your enums.

For more details about enum support and the features listed above, feel free to check out the documentation.

SDK download

When using Data Connect, you use GraphQL to model your data and define CRUD operations. To actually get data and make changes from your client app, GraphQL needs to be translated into the language for the platform you’re developing. Data Connect does this automatically; it generates strongly-typed SDKs for web, Android, iOS, and Flutter based on the operations you have defined.

Previously, you had to set this up through the Firebase CLI and run a command to generate the SDKs. Now, you can do this more seamlessly using a script that downloads the SDKs for you! To do so, you can use:

  • the project-specific command found in Firebase Console as shown below
  • a generic command that works for all projects
  • the VS Code extension to generate client SDKs

To learn more about downloading the SDK for new projects that don’t have Data Connect set up yet, feel free to check out the documentation.

MCP Support

By connecting to Model Context Protocol (MCP) servers, coding agents can directly integrate with additional tools and services in a standardized fashion, extending the capabilities of AI tools. You can use the Firebase MCP server to give AI-powered development tools the ability to work with your Firebase projects.

With regards to Data Connect, an editor configured to use the Firebase MCP server can help you work with data and retrieve schemas, and some of the tools use Gemini in Firebase to help you generate schemas and operations.

To make MCP even more widely available, Gemini Code Assist released their MCP integration last month. Using Agent Mode, you can converse naturally and the agent will determine the proper MCP tools to use. The “Build your schema and queries with AI” button adds the Firebase MCP server if you haven’t already before taking you directly to Agent Mode.

Production Web Architecture & Security Checklist

Whether we are deploying on Firebase App Hosting, Cloudflare Workers, or Vercel, shipping AI features to production requires defense-in-depth across three layers:

  • Enforce Firebase App Check & Attestation: Protect your web endpoints, Cloud Functions, Data Connect GraphQL resolvers, and Firebase AI Logic calls against unauthorized replay or billing abuse using Firebase App Check with reCAPTCHA Enterprise.
  • Validate Locally with the Firebase Emulator Suite: Run deterministic integration tests against local Firestore, Auth, Functions, and Data Connect PostgreSQL emulators before promoting schema or rule changes to production.
  • Keep Server & Client Boundaries Type-Safe: Use the modular Firebase Web SDK and generated TypeScript bindings so SSR frameworks like Next.js, Astro, SvelteKit, and Angular serialize state cleanly without hydration flickers.

Conclusion

Accelerate your full-stack web and AI development by pairing Data Connect: Full Text Search, Enums, and More with strict Security Rules, Firebase App Check, and local emulator testing. Come tell us on X (@CodingCatDev) and LinkedIn (CodingCat.dev) how you are using Firebase in your stack, and subscribe to the CodingCat.dev YouTube Channel so you don't miss what we build next!

Watch Our Related Firebase Video Tutorial on YouTube

Want to see how we architect and debug Firebase web applications live? Watch our related CodingCat.dev session Firebase SQL with Data Connect below, and make sure to Subscribe to CodingCat.dev on YouTube for weekly full-stack web and AI deep dives:

Resources, Official Docs & Related CodingCat.dev Guides

Frequently Asked Questions (FAQ)

What are the key capabilities introduced in Data Connect: Full Text Search, Enums, and More?

Data Connect: Full Text Search, Enums, and More (announced 2025-07-23 by the Firebase team) enhances the platform for Firebase, Google Cloud, Web Dev, AI, MCP, SQL, TypeScript workloads. Note: Firebase Data Connect has been renamed to SQL Connect. Learn more in the SQL Connect announcement. Developers can review the primary announcement on the Firebase Blog alongside our implementation patterns and production checklist above.

How do web developers use Data Connect: Full Text Search, Enums, and More with Next.js, SvelteKit, React, or Angular?

Web developers can integrate these features using the modular Firebase JavaScript/TypeScript SDK, Firebase Data Connect web bindings, and Firebase App Hosting. Always pair client-side SDK initialization with Firebase App Check and strict Security Rules, and test locally with the Firebase Local Emulator Suite before deploying.

Where can I watch video tutorials on Firebase Web and AI development?

You can watch our hands-on Firebase video walkthroughs on the CodingCat.dev YouTube Channel, including Firebase SQL with Data Connect, and follow us on X (@CodingCatDev) for weekly full-stack tutorials.