GCP – Announcing MCP Toolbox support for Firestore

MCP Toolbox for Databases (Toolbox) is an open-source MCP server that makes it easy for developers to connect gen AI agents to enterprise data, with initial support for databases like BigQuery, AlloyDB, Cloud SQL, and Spanner. Since launching earlier this year, Toolbox has made it easier for millions of developers to access enterprise data in databases.

Today, we’re expanding Toolbox with a comprehensive new set of tools for Firestore. This will help developers build more modern web and mobile applications. Let’s explore how these new capabilities can improve your development process.

What MCP is, and how it unlocks AI-assisted workflows

MCP is an emerging open standard for connecting AI systems with tools and data sources through a standardized protocol, replacing fragmented, custom integrations. Think of MCP as a universal adapter for AI, allowing any compatible assistant to plug into any tool or database without needing a custom-built connector each time. Now with the MCP Toolbox, these assistants (such as those in an IDE or a CLI like the Gemini CLI) can connect directly to your Firestore database.

This is a massive step for AI-assisted workflows, from debugging data and testing security rules to managing your collections—all using natural language. For instance, a developer building a retail app can now ask their assistant, ‘Find all users whose wishlists contain discontinued product IDs,’ to perform data cleanup, without writing a single line of code.

Let’s explore how these new capabilities can improve your development process.

AI-assisted development meets the NoSQL world

As you carry out AI-assisted tasks, you’re probably looking for the most efficient way to interact with your data. Our new pre-built tools for Firestore enable you to do just that, directly from your Gemini CLI or other AI-powered development environment.

Firestore’s flexible document structure and powerful security rules offer unique capabilities for building modern mobile and web applications. These tools are crafted to empower the Firestore developer, helping them master both the flexibility of the document model and the creation of robust access controls that protect their app. You can now use your AI assistant to perform queries, carry out targeted document updates, and even validate your security rules before you deploy them, saving you time and preventing errors.

From QA bug to resilient feature: A developer’s story

Let’s take a hypothetical example. Alex is a full-stack developer on a team building a new e-commerce application using Firestore. She uses  Gemini CLI to help her code, debug, and test. This morning, a high-priority bug was filed by the QA team: an issue in the staging environment is causing items to reappear in a user’s “wishlist” after being removed. Because of the blog, her release was blocked.

The bug hunt begins

Until now, investigating a bug meant Alex would have to manually click through the Cloud Console to inspect test documents or write a custom script just to query the staging database—a slow and cumbersome process. Now, she can simply have a conversation with Gemini CLI.

The bug report contains the test user accounts. Alex opens her terminal and asks:

“Hey, show me the Firestore data for the test users qa_user_123 and qa_user_456 from the users-staging collection.”

Gemini CLI understands this, calls the firestore-get-documents tool, and instantly displays the JSON for both user documents. Alex confirms the bug—the wishlist array contains stale data. She continues the conversation to understand the scope:

“Okay, that’s the bug. Find all users in the users-staging collection whose wishlist contains product-glasses(inactive).”

CLI uses the firestore-query-collection tool and reports back that 20 test accounts are affected. After developing a code fix, she needs to clean the test environment to verify it.

“For all 20 test users you just found, please remove product-glasses(inactive) from their wishlist.”

Gemini CLI confirms the plan and uses the firestore-update-document tool to perform the cleanup, clearing the way for a successful re-test.

From reactive fix to proactive hardening

The immediate bug is fixed, but Alex wants to ensure this class of error can never happen again. She decides to enforce the correct data structure with Firestore Security Rules.

Until now, validating security rules meant a disruptive context switch. Alex would have to copy her rules, navigate away from her terminal to the Firebase Console’s Rules Playground, or set up a local emulator just to check for syntax errors. This friction often discourages thorough, iterative testing within the main development loop.

Now, Alex drafts her new, stricter security rule. Before deploying, she asks Gemini CLI for a pre-flight check right from her terminal:

“new_rules.txt is a new Firestore Security Rule I’m working on for staging. Can you validate it for me?” 

CLI uses the firestore-validate-rules tool and replies, “The issue is a missing semicolon at the end of the return statement” Alex fixes the typo instantly. For a final check, she asks:

“Show me the active Firestore security rules for this project.”

Using the firestore-get-rules tool,  CLI displays the current ruleset, allowing Alex to do a final comparison. Confident and assured, she deploys her changes.

A task that could have taken hours of manual investigation, scripting, and context switching was completed in minutes. Alex didn’t just fix a bug; she used her AI assistant to make the entire application more resilient.

Getting started

These new Firestore tools within the MCP Toolbox represent our commitment to providing you with powerful, intuitive tools that accelerate the entire development lifecycle. By connecting your Firestore database directly to your AI assistant, you can spend less time on tedious tasks and more time building incredible applications.

Learn more about MCP Toolbox for Databases, connect it to your favorite AI-assisted coding platform, and experience the future of AI-accelerated, database-connected software development today.

• Docs: Connecting your AI Assistant to Toolbox
• Quick Start Guide: Using Firestore Tools w/ MCP
• Contribute to MCP Toolbox
• Join our Discord community