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Google Cloud Firestore MCP server

Create a powerful Model Context Protocol (MCP) server for Google Cloud Firestore to manage NoSQL data, perform real-time queries, and synchronize data across applications. This integration enables AI agents to automate database operations, manage collections and documents, execute transactions, and maintain data quality with secure service account authentication.

Setting up an MCP server

This article covers the standard steps for creating an MCP server in AI Gateway and connecting it to an AI client. The steps are the same for every integration — application-specific details (API credentials, OAuth endpoints, and scopes) are covered in the individual application pages.

Before you begin​

You'll need:

  • Access to AI Gateway with permission to create MCP servers
  • API credentials for the application you're connecting (see the relevant application page for what to collect)

Create an MCP server​

Find the API in the catalog​

  1. Sign in to AI Gateway and select MCP Servers from the left navigation.
  2. Select New MCP Server.
  3. Search for the application you want to connect, then select it from the catalog.

Configure the server​

  1. Enter a Name for your server — something descriptive that identifies both the application and its purpose (for example, "Zendesk Support — Prod").
  2. Enter a Description so your team knows what the server is for.
  3. Set the Timeout value. 30 seconds works for most APIs; increase to 60 seconds for APIs that return large payloads.
  4. Toggle Production mode on if this server will be used in a live workflow.
  5. Select Next.

Configure authentication​

Enter the authentication details for the application. This varies by service — see the Authentication section of the relevant application page for the specific credentials, OAuth URLs, and scopes to use.

Configure security​

  1. Set any Rate limits appropriate for your use case and the API's own limits.
  2. Enable Logging if you want AI Gateway to record requests and responses for auditing.
  3. Select Next.

Deploy​

Review the summary, then select Deploy. AI Gateway provisions the server and provides a server URL you'll use when configuring your AI client.

Connect to an AI client​

Once your server is deployed, you'll need to add it to the AI client your team uses. Select your client for setup instructions:

Tips​

  • You can create multiple MCP servers for the same application — for example, a read-only server for reporting agents and a read-write server for automation workflows.
  • If you're unsure which OAuth scopes to request, start with the minimum read-only set and add write scopes only when needed. Most application pages include scope recommendations.
  • You can edit a server's name, description, timeout, and security settings after deployment without redeploying.

Authentication​

Google Cloud Firestore uses OAuth 2.0 with service accounts for API access. You'll create a service account in your Google Cloud project and download a JSON key file. The service account needs the Cloud Datastore User role (or permissions like datastore.entities.create, datastore.entities.update, datastore.entities.delete, datastore.entities.list). The Google OAuth endpoint is https://oauth2.googleapis.com/token, and the integration requires scope https://www.googleapis.com/auth/cloud-platform for full Firestore access.

Available tools​

These tools let AI agents perform full CRUD operations on Firestore documents, execute queries, manage collections, and handle transactions. Together they enable building data-driven applications with real-time synchronization and complex querying.

ToolDescription
Get documentRetrieve a single document by ID
Create documentCreate a new document in a collection
Update documentModify an existing document
Delete documentRemove a document
List documentsQuery all documents in a collection with filters
Query documentsExecute complex queries with multiple conditions
Batch getRetrieve multiple documents by ID in one call
Batch writeCreate, update, or delete multiple documents atomically
Begin transactionStart a multi-step atomic operation
Commit transactionSave all changes from a transaction
Rollback transactionDiscard a transaction if validation fails
Export databaseCreate a backup by exporting all data
Import databaseRestore data from a previous export
Create indexOptimize queries with composite indexes
List collectionsView all collections in the database

Tips​

Use Firestore's transactional writes to keep data consistent across collections — for example, atomically update both an order and inventory counts together.

Use batch operations when importing or updating large datasets instead of individual writes to improve performance and reduce costs.

Create composite indexes for queries that filter by multiple conditions — without indexes, Firestore scans all documents, which is slow and expensive.

Use transactions to validate data before writing — for example, check that an order total matches the sum of line items before committing.

Periodically export your database to Cloud Storage for disaster recovery and long-term data archival.

Use Firestore's built-in backup and restore for point-in-time recovery when you need to recover recent data.