How to Use the Digify MCP in LlamaIndex

Q: How does Digify integrate with LlamaIndex vector stores?

You call listfiles to fetch your document directory, then index that metadata into a VectorStoreIndex. This lets your RAG pipeline search across secure document titles.

Q: Can LlamaIndex agents invite users to Digify data rooms?

Yes, by using the inviteguest tool. The agent evaluates user queries and automatically issues invites to the correct email addresses.

Q: Does the Digify MCP Server support asynchronous calls in LlamaIndex?

Yes. You use totoollistasync with McpToolSpec to load the tools. This prevents blocking your main query pipeline when fetching large activity lists.

Q: Can I filter which tools my LlamaIndex agent can access?

Yes, you use the allowedtools filter during initialization. This lets you restrict an agent to reading stats while blocking file protection tools.

Index secure document rooms and query file telemetry with this MCP Server in LlamaIndex RAG pipelines.

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Connect Digify MCP to LlamaIndex

Create your Vinkius account to connect Digify 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 Digify with LlamaIndex

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Query secure document rooms inside LlamaIndex

Use `list_files` and `get_file` to pull metadata and file structures directly into your vector store. Your RAG pipeline indexes this data so users can search across protected document titles and access levels. This grounds your search results in actual permissions. The agent only retrieves files the current user has rights to view.

Analyze engagement data with this MCP Server

Call `get_dataroom_stats` and `list_activities` to ingest raw viewer telemetry into your LlamaIndex query engine. Your agent indexes these logs, letting you ask questions about which pages got the most attention. You get direct answers about user behavior. The model queries the vector store to find which partners reviewed the contract details.

Automate user onboarding via LlamaIndex agents

The `invite_guest` and `list_dataroom_guests` tools let your agents manage access control lists based on search context. When a query identifies an authorized user missing from a room, the agent invites them. This creates a closed loop for document access. Your index stays synchronized with actual room participation without manual intervention.

Setup guide

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

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Common questions about Digify MCP in LlamaIndex

You call `list_files` to fetch your document directory, then index that metadata into a VectorStoreIndex. This lets your RAG pipeline search across secure document titles.

Yes, by using the `invite_guest` tool. The agent evaluates user queries and automatically issues invites to the correct email addresses.

Yes. You use `to_tool_list_async` with `McpToolSpec` to load the tools. This prevents blocking your main query pipeline when fetching large activity lists.

Yes, you use the `allowed_tools` filter during initialization. This lets you restrict an agent to reading stats while blocking file protection tools.

Your document activity logs are fetched via the MCP Server and indexed locally in your vector database. Vinkius operates a zero-trust sandbox, meaning no telemetry is cached or stored on our servers.

Use it with your favorite AI tools

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