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Vinkius

Google Cloud Functions Connector for AI agents.

1 live capability

Run secure serverless compute tasks from your AI client.

Live agent request Google Cloud Functions / Connector

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AI Agent

Why people use Google Cloud Functions

Google Cloud Functions for Secure Backend Infrastructure

This Connector solves that by creating a surgical bridge. Instead of broad permissions, you point the agent at one specific function. It can run your heavy lifting, hit your private services, and return the data you need without ever seeing the rest of your infrastructure.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a secure bridge to run specific cloud logic without opening your whole account to your AI client.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 6,100+ Connectors

  1. Real-world use case 01

    Secure Image Processing

    An agent receives a request to resize a large image.

  2. Real-world use case 02

    Private Data Scraping

    A user asks for data from a private internal site.

  3. Real-world use case 03

    Offloading Heavy Math

    A user provides a large dataset ID.

Complete set · 1capability

The complete Google Cloud Functions capability set.

These are the exact actions your AI can choose when you ask it to work with Google Cloud Functions.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through Google Cloud Functions.

  1. 01 Capability

    Invoke function

    Use this to execute remote business logic or heavy processing tasks. Invoke the configured Google Cloud Function

Set up in minutes

One URL. Then ask Google Cloud Functions to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Google Cloud Functions from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_ot6QbSG45dUkURNWnf6YGaSHZvntFNxbPzDeJA49/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Google Cloud Functions, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Google Cloud Functions for the conversation.

Where the request belongs

Work Google Cloud Functions can move forward.

Built around the request

This is for engineers who need to bridge the gap between AI capabilities and private cloud infrastructure without compromising security.

01

DevOps Engineer

They use this to let the AI run specific maintenance scripts or infrastructure checks without giving it full root access.

02

Data Scientist

They use this to trigger heavy data crunching tasks on large datasets that would otherwise time out in a standard chat window.

03

Backend Developer

They use this to let the agent interact with private internal services that are shielded from the public web.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Azure Functions Invoke logo
01 1 capability

Azure Functions Invoke

This MCP does exactly one thing: it invokes a single Azure Function. That's its only function, and nothing else. Incredible for letting your AI execute secure serverless compute.

View Connector
Amazon Lambda Invoke logo
02 1 capability

Amazon Lambda Invoke

This MCP does exactly one thing: it invokes a single AWS Lambda function. That's its only function, and nothing else. Incredible for letting your AI execute secure serverless compute.

View Connector
YepCode logo
03 6 capabilities

YepCode

Run serverless code snippets in the cloud with a platform that lets you build integrations and automations in any programming language.

View Connector
Modal (Serverless AI Infrastructure) logo
04 7 capabilities

Modal (Serverless AI Infrastructure)

Manage serverless compute via Modal. audit active apps, track GPU deployments, and monitor network volumes.

View Connector
Lambda Labs (GPU Cloud) logo
05 7 capabilities

Lambda Labs (GPU Cloud)

Manage AI infrastructure via Lambda Labs. launch GPU instances, monitor ML workloads, and manage SSH keys.

View Connector
RunPod logo
06 7 capabilities

RunPod

Integrate your AI securely to RunPod to cleanly quickly provision scalable GPU pods, manage active instances, and inspect serverless endpoints and custom templates natively.

View Connector

Bring your own AI

Change the model, client or framework. Keep Google Cloud Functions connected.

  • 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 Cloud Functions.

The practical details behind the request, access and result.

How does Google Cloud Functions keep my account secure?

It limits your AI client to a single, specific function. Your agent can't browse your files, delete your projects, or see other functions because it only has permission to hit one endpoint.

Can I use Google Cloud Functions for heavy data tasks?

Yes, that is a primary use case. You can offload large datasets to a serverless environment to handle the math or processing, then have the agent report the final answer to you.

Does this work with Claude or Cursor?

Yes, this Connector works with any compatible client, including Claude, Cursor, and Windsurf, allowing your agent to trigger cloud actions naturally.

What happens if the function takes a long time to run?

The agent will wait for the function to return a response. For very long tasks, it's best to use an asynchronous pattern where the agent checks the status of a job.

Do I need to write my own API for this?

No, you just need an existing Google Cloud Function. This Connector acts as the bridge that lets your agent talk to that function directly.

Can my agent access my private data through this?

It can access whatever data your specific function is allowed to see. If your function pulls from a private database, the agent can receive that data safely through the function.

Why limit the agent to a single Cloud Function?

To enforce zero-trust security. An autonomous AI agent should not have the ability to execute arbitrary serverless functions (like wiping a database or sending mass emails) across your cloud infrastructure.

How are responses handled?

The Connector will automatically parse valid JSON responses returned by the Cloud Function. If the function returns an error or a timeout, the execution ID and the specific error string will be returned to the agent.

Can it invoke Gen 2 Cloud Functions?

Yes! The capability uses the standard Google Cloud Functions REST API (:call endpoint), which is compatible with both 1st gen and 2nd gen functions, provided the IAM Service Account has the roles/cloudfunctions.invoker permission.

One connection away

Give your agent a direct line to Google Cloud Functions.

Connect Google Cloud Functions once. Keep it beside 6,100+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available