How to Use the Google Cloud Storage Bucket MCP in LlamaIndex

Q: Can I filter files in my Google Cloud Storage Bucket via LlamaIndex?

The listobjects tool returns file metadata. You can filter this list in your agent code before deciding which objects to retrieve.

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Connect Google Cloud Storage Bucket MCP to LlamaIndex

Create your Vinkius account to connect Google Cloud Storage Bucket to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Google Cloud Storage Bucket with LlamaIndex

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Index Google Cloud Storage Bucket files

LlamaIndex treats your Google Cloud Storage Bucket as a dynamic data source. Use `list_objects` to discover files and ingest them directly into your vector store. This creates a searchable knowledge base that updates as your files change. Your RAG application stays grounded in your actual cloud data.

Retrieve context from cloud storage

Fetch specific files using `get_object` to provide deep context to your agent. This is how you ground your answers in real-world documents stored in the cloud. By converting these to tools, LlamaIndex can pull content on demand. It avoids hallucinations by relying on the raw data you provide.

Update knowledge with new objects

Use `put_object` to save generated insights or processed documents back to your storage. This closes the loop between your LlamaIndex agent and your storage infrastructure. Every write operation is a direct interaction with your bucket. Your agent keeps the storage state current without manual file transfers.

Setup guide

Set up Google Cloud Storage Bucket MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Google Cloud Storage Bucket MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Google Cloud Storage Bucket tools.",
)
response = await agent.run("List recent Google Cloud Storage Bucket data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Google Cloud Storage Bucket. 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.

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Common questions about Google Cloud Storage Bucket MCP in LlamaIndex

It lists files via the MCP tool and retrieves content for indexing. You can then perform semantic searches over the content of those files.

The `list_objects` tool returns file metadata. You can filter this list in your agent code before deciding which objects to retrieve.

The server handles the download, but consider memory limits. If your files are massive, process them in chunks or use metadata indexing first.

Configure your environment with a service account that has limited access. The MCP server uses these credentials to perform its work.

Your data never leaves your infrastructure. The MCP server acts as a bridge, and no third party stores your object content.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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