Google Firestore Collection Connector for AI agents.
3 live capabilities
Give your agent a dedicated NoSQL database for structured data storage.
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Why people use Google Firestore Collection
Google Firestore Collection NoSQL Database Integration
With this Connector, the agent just talks to the database directly. You tell it to save that result, and it handles the write operation in the background. You get a persistent memory system without the dev work.
What Vinkius changes
You get a secure, dedicated sandbox for your agent's NoSQL data.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Persistent Chat History
A user wants their agent to remember past conversations.
- Real-world use case 02
Real-time Task Tracking
An ops manager needs an agent to update ticket statuses.
- Real-world use case 03
Dynamic User Profiles
A developer needs an agent to fetch user preferences.
Complete set · 3capabilities
The complete Google Firestore Collection capability set.
These are the exact actions your AI can choose when you ask it to work with Google Firestore Collection.
01—03
3 capabilities in this set.
Part of 3 available through Google Firestore Collection.
- 01 Capability
Delete document
Remove a specific document from your collection to clear out old data. This is useful for cleaning up temporary files or expired records.
- 02 Capability
Get document
Fetch the details of a single record using its unique identifier. Use this to pull specific user data or check the status of a task.
- 03 Capability
Set document
Create a new entry or update the fields of an existing one in your collection. This lets your agent save new information or modify existing records.
Set up in minutes
One URL. Then ask Google Firestore Collection to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Google Firestore Collection from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Google Firestore Collection, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Google Firestore Collection for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Google Firestore Collection URL.
- Step 03
Save and start
Save the connection and enable Google Firestore Collection in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"google-firestore-collection": {
"url": "https://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Google Firestore Collection
Open Agent mode in chat and ask: "Using Google Firestore Collection, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"google-firestore-collection": {
"url": "https://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Google Firestore Collection
Ask Copilot: "Using Google Firestore Collection, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"google-firestore-collection": {
"url": "https://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Google Firestore Collection
Open Cascade and ask: "Using Google Firestore Collection, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"google-firestore-collection": {
"url": "https://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Google Firestore Collection
Ask Cline: "Using Google Firestore Collection, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add google-firestore-collection --transport http "https://edge.vinkius.com/vk_preview_D9PPFFrmux8dXozdC2CFNnwDeOgr9v7H0CtVCBNC/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Google Firestore Collection
Ask Claude: "Using Google Firestore Collection, show me...". 3 tools are ready
Where the request belongs
Work Google Firestore Collection can move forward.
This is for developers and engineers who need to give their AI agents a persistent memory or a way to write to a database without creating a massive security headache or building custom APIs.
Backend Engineer
Building an agent that needs to write to a database without creating a massive security headache.
Product Manager
Setting up a prototype that requires persistent memory for user interactions.
AI Researcher
Testing how agents handle structured data in a cloud environment.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsFauna (Serverless DB)
Execute FQL queries directly against your Fauna serverless database from any AI agent to manage collections, documents, and indexes.
Firebase (REST & Admin APIs)
Manage Firebase Realtime Database, Firestore, and Auth via REST APIs. query data, manage users, and send FCM messages directly from your AI agent.
Couchbase (Vector & NoSQL)
Manage vector search and NoSQL via Couchbase. execute N1QL queries, perform KNN vector searches, and audit documents directly from any AI agent.
DataStax Astra DB Vector
Manage Astra DB collections, documents, and perform vector similarity searches via DataStax directly from your AI agent.
LanceDB (Serverless Vector DB)
Manage vectorized data via LanceDB. perform similarity searches, create tables, and manage multi-modal embeddings.
Feishu Bitable
Collaborative multi-dimensional table platform by Lark. manage bases, tables, and records via AI.
Bring your own AI
Change the model, client or framework. Keep Google Firestore Collection connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Google Firestore Collection.
The practical details behind the request, access and result.
What does Google Firestore Collection do for my AI?
It gives your agent a dedicated spot in your database to store and retrieve structured data like user preferences or logs.
Is it safe to let my agent access my database?
Yes, because this Connector is restricted to one specific collection, it can't touch your other production data.
Can my agent remember things from yesterday?
Yes, by using the storage capabilities, your agent can save information to the collection and pull it back during future chats.
Do I need to write code to use this?
No, you just connect the Connector to your AI client and tell the agent what to save or find.
What kind of data can I store?
You can store any structured data, including JSON objects, status updates, and app state.
How does this help with app state?
It allows your agent to keep track of where a user is in a multi-step process by updating a document in real-time.
Why limit the agent to a single Firestore Collection?
To enforce zero-trust security. An autonomous AI agent storing its task logs shouldn't have access to query or modify critical user data in other collections.
How are JSON types converted to Firestore fields?
The capability automatically performs a basic mapping. Strings become stringValue, integers become integerValue, and booleans become booleanValue. Complex nested objects may be serialized as strings.
Can I query multiple documents at once?
No. To maintain deterministic behavior, this capability is designed for key-value (document ID) access patterns. If you need complex queries, consider a custom BigQuery MCP.
One connection away
Give your agent a direct line to Google Firestore Collection.
Connect Google Firestore Collection once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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