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.
Source Credit: https://cloud.google.com/blog/products/ai-machine-learning/firestore-support-and-custom-tools-in-mcp-toolbox/
