Can I upload large files directly through this server?

The `upload_object` tool is designed for small to medium text-based content. For very large binary files or production datasets, it is recommended to use the gsutil CLI or the Google Cloud Console to ensure optimal transfer speeds and integrity.

How do I check if a bucket is publicly accessible?

You can use the `list_bucket_acl` or `get_bucket_iam` tools. The AI agent will retrieve the permissions and can identify if roles like 'allUsers' or 'allAuthenticatedUsers' have been granted access.

Can I move files between different buckets?

Yes! Use the `copy_object` tool to copy an object from a source bucket to a destination bucket. To 'move' it, you would typically copy the object first and then use `delete_object` on the source.

Google Cloud Storage MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Google Cloud Storage
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Google Cloud Storage as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Google Cloud Storage. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Google Cloud Storage?"
    )
    print(response)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Google Cloud Storage MCP Server

Connect your Google Cloud Storage project to your AI agent and streamline your cloud data management. Use natural language to browse buckets, inspect file metadata, manage object lifecycles, and audit security permissions across your global storage infrastructure.

LlamaIndex agents combine Google Cloud Storage tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Bucket Exploration — List all buckets in your project and retrieve detailed metadata including location and storage class
Object Management — Browse files within buckets using prefixes (folders), view sizes, and delete or copy objects effortlessly
Data Operations — Upload text-based content directly or initiate object copies between buckets via simple commands
Security Auditing — Check Access Control Lists (ACLs) and IAM policies for both buckets and individual objects to ensure compliance
Project Insights — Retrieve service account details and manage HMAC keys for legacy or cross-cloud integrations

The Google Cloud Storage MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Google Cloud Storage to LlamaIndex via MCP

Follow these steps to integrate the Google Cloud Storage MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Google Cloud Storage

Why Use LlamaIndex with the Google Cloud Storage MCP Server

LlamaIndex provides unique advantages when paired with Google Cloud Storage through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Google Cloud Storage tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Google Cloud Storage tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Google Cloud Storage, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Google Cloud Storage tools were called, what data was returned, and how it influenced the final answer

Google Cloud Storage + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Google Cloud Storage MCP Server delivers measurable value.

Hybrid search: combine Google Cloud Storage real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Google Cloud Storage to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Google Cloud Storage for fresh data

Analytical workflows: chain Google Cloud Storage queries with LlamaIndex's data connectors to build multi-source analytical reports

Google Cloud Storage MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Google Cloud Storage to LlamaIndex via MCP:

copy_object

Copy an object within or between buckets

delete_object

Remove an object from a bucket

get_bucket_iam

Get IAM policy for a bucket

get_bucket_metadata

Get metadata for a specific bucket

get_object_metadata

Get metadata for a specific object (file)

get_project_service_account

Check the storage service account for the project

list_bucket_acl

Check bucket permissions

list_buckets

List all buckets in the project

list_hmac_keys

List HMAC keys for a service account

list_object_acl

Check permissions for a specific object

list_objects

List objects within a bucket

upload_object

Upload a new file to a bucket

Example Prompts for Google Cloud Storage in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Google Cloud Storage immediately.

"List all buckets in my Google Cloud project."

"Find all files in bucket 'prod-assets' that start with 'images/2024/'."

"Check who has access to the 'user-uploads-data' bucket."

Troubleshooting Google Cloud Storage MCP Server with LlamaIndex

Common issues when connecting Google Cloud Storage to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Google Cloud Storage + LlamaIndex FAQ

Common questions about integrating Google Cloud Storage MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Google Cloud Storage tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.