How to Use the Azure Blob Container MCP in LlamaIndex

Q: Does LlamaIndex support listing files in an Azure Blob Container?

Yes, the listblobs tool allows the agent to browse your storage. You can set prefixes to filter the files the agent sees.

Q: Can LlamaIndex update files in an Azure Blob Container?

Use putblob to overwrite or create files. LlamaIndex treats these as new data sources once they are indexed.

Index your Azure Blob Container files directly into LlamaIndex for grounded semantic search.

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Connect Azure Blob Container MCP to LlamaIndex

Create your Vinkius account to connect Azure Blob Container 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 Azure Blob Container with LlamaIndex

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LlamaIndex MCP Server ingestion

Turn your blob storage into a searchable knowledge base. Use `get_blob` to retrieve file contents that LlamaIndex then indexes for your RAG applications. This setup grounds your agent's answers in actual file data. It stops hallucinations by forcing the agent to query the index before responding.

Automated file discovery in LlamaIndex

Use `list_blobs` to scan your container for new documents. The agent identifies which files require indexing and adds them to your vector store. This keeps your knowledge base current without manual updates. The agent handles the scanning logic based on your specific index requirements.

Direct file management for LlamaIndex

Write new summaries or metadata back to your storage using `put_blob`. LlamaIndex can then re-index these files to reflect your latest insights. This creates a closed-loop system where the agent learns, summarizes, and stores its own conclusions. You maintain full control over the storage lifecycle.

Setup guide

Set up Azure Blob Container 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 Azure Blob Container 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 Azure Blob Container tools.",
)
response = await agent.run("List recent Azure Blob Container data")

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Common questions about Azure Blob Container MCP in LlamaIndex

Initialize the MCP client and pass the tool list to your agent. LlamaIndex will process the tool outputs and build your vector index.

Yes, the `list_blobs` tool allows the agent to browse your storage. You can set prefixes to filter the files the agent sees.

Use `put_blob` to overwrite or create files. LlamaIndex treats these as new data sources once they are indexed.

Your storage access is restricted by the endpoint token provided. Only the initialized LlamaIndex client can request file content.

All file contents are kept within your local environment. Data is indexed locally and never shared outside your private instance.

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