Vinkius

Firestore Collection MCP. Give your agent a contained memory bank for structured data.

Google Firestore Collection MCP gives your AI agent a secure, dedicated NoSQL database for structured data storage. It lets your client perform precise operations like querying, creating new records, and updating existing documents within one specific Google Firestore collection. This is perfect for giving agents a safe place to track chat histories or process project states without touching critical cloud databases.

Firestore Collection MCP is compatible with Claude Claude
Firestore Collection MCP is compatible with ChatGPT ChatGPT
Firestore Collection MCP is compatible with Cursor Cursor
Firestore Collection MCP is compatible with Gemini Gemini
Firestore Collection MCP is compatible with Windsurf Windsurf
Firestore Collection MCP is compatible with VS Code VS Code
Firestore Collection MCP is compatible with JetBrains JetBrains
Firestore Collection MCP is compatible with Vercel Vercel
See Vinkius in Action

Give Claude and any AI agent real-world access

Read data records

Your AI client reads an entire document or specific fields from the configured Firestore collection.

Write new documents

The agent creates a brand-new record in the collection, assigning it all necessary structured information.

Update existing records

Your AI client modifies specific fields within an already existing document without affecting other data points.

Remove old documents

The agent deletes a targeted record from the collection when it's no longer needed.

Waiting for input…

AI Agent
Firestore Collection

What AI agents can do with Google Firestore Collection: 3 Tools

These tools let you perform the fundamental operations needed to manage data persistence within a single Google Firestore collection.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Google Firestore Collection MCP

Delete Document

This tool removes an entire document from the specified Firestore collection by its ID.

Get Document

It retrieves all the field data associated with a specific document ID within the...

Set Document

This tool creates a new document or updates an existing one in the collection...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Firestore Collection MCP is compatible with Claude

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Firestore Collection integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on each call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Google Firestore Collection, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,200+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Connections are secured and governed automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog weekly
Firestore Collection MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Google Firestore Collection. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS CLOUD

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on each call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

The Pain of Ephemeral Agent Memory

Today, when a user interacts with an agent over several sessions, all the conversation history and resulting data—the key decisions, the final score, the temporary notes—must be kept in the prompt context. This is clunky; you're limited by token counts, and if the chat gets too long, the core information falls out of scope or costs a fortune to run.

With this MCP, your agent doesn't rely on its short-term memory (the conversation window). Instead, it uses tools like `set_document` to write critical context into Firestore. The data is saved externally, meaning you can retrieve exactly what you need later using `get_document`, no matter how many days pass.

Managing Documents with the Google Firestore Collection MCP

You eliminate the manual step of copying conversation summaries into a separate spreadsheet or manually re-running complex workflows just to save the output. The agent handles all the persistence steps automatically.

It's reliable, contained, and structured. You get true data ownership that keeps your AI workflows stable and scalable over time.

What Firestore Collection MCP does for your AI

Your AI agent suddenly gets a dedicated memory bank. Instead of forcing it to store complex data in messy text blocks, this MCP gives your client the ability to manage structured information directly inside one Firestore collection. Think of it as a single, protected filing cabinet for all your project's temporary or persistent data.

It strips away dangerous global database permissions, giving your agent only surgical access to that specific spot. Your AI can safely read documents, write new ones, and modify fields in place—all without the risk of damaging other parts of your cloud setup. This controlled environment is huge for building reliable agents.

When you connect this MCP via Vinkius, you get instant, contained database power for anything from storing chat threads to running complex workflow results.

It’s a simple, scalable NoSQL connection that lets your agent behave like it has its own internal memory and state machine.

Built · Hosted · Managed by Vinkius Firestore Collection MCP - Manage Structured NoSQL Data
Server ID 019e38a2-a6e4-7309-8f2a-0180a5f41841
Vinkius Inspector
Compliance Grade F
Score 3.6/100
Vinkius Inspector Badge — Score 3.6/100

Frequently asked questions about Firestore Collection MCP

Does Google Firestore Collection MCP support complex SQL joins? +

No, it is designed for NoSQL document operations. It manages individual records within one collection using tools like get_document and set_document, not relational joins.

Is my data secure when I use the Google Firestore Collection MCP? +

Yes, security is paramount. This MCP limits your agent's access to a single collection only, preventing it from touching other sensitive parts of your cloud infrastructure.

How do I delete old chat logs using the Google Firestore Collection MCP? +

You use the delete_document tool. You simply provide the unique document ID for the chat session, and the agent removes that entire record from the collection.

Can this MCP store structured JSON data? +

Absolutely. The primary function of the MCP is to allow you to write rich, structured data—like workflow results or user profiles—using set_document into the NoSQL format.

What if I need to update only one field in a document? +

You use the set_document tool. This allows you to target and modify specific fields within an existing record without overwriting all the other data points.