How to Use the Digify MCP in LlamaIndex

Q: How do I handle expired files in my LlamaIndex knowledge base?

Your agent runs listexpiredsecurefiles to identify dead documents and clean up your index. This ensures your RAG application doesn't recommend or reference files that are no longer accessible to users.

Index secure document metadata and access stats into LlamaIndex to query your Digify history with zero hallucinations.

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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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Index secure file metadata in LlamaIndex

The `get_secure_file_details` tool fetches Digify security settings and file properties to feed directly into your LlamaIndex vector store. This allows your LlamaIndex RAG pipeline to ground its answers in actual Digify document settings instead of guessing details. Your LlamaIndex agent queries this indexed Digify data to find out which files are protected and who has permission to view them. This approach prevents your LlamaIndex LLM from hallucinating Digify file security parameters.

Query data room structures using LlamaIndex MCP Server tools

The `list_virtual_datarooms` tool retrieves all active virtual data rooms in your Digify account so LlamaIndex can build a semantic map of your workspace. Your LlamaIndex agent uses this index to locate where specific Digify project files live without manual searching. By combining this with `get_dataroom_details`, your LlamaIndex index stays updated with the latest Digify member lists and access policies. Users query LlamaIndex using natural language to find out who has eyes on which Digify room.

Analyze document engagement trends in LlamaIndex

The `get_file_access_statistics` tool extracts detailed viewing durations and recipient activity logs for your indexed Digify files within LlamaIndex. Your LlamaIndex agent analyzes these Digify statistics to identify which sections of your pitch deck or contract got the most attention. This live Digify data merges with your LlamaIndex knowledge base to provide real-time updates on recipient behavior. You get direct answers about Digify document engagement inside LlamaIndex without digging through raw CSV exports.

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 initialize the LlamaIndex MCP client and convert it using the tool specification helper. Your LlamaIndex agent then calls `list_secure_files` or `search_secure_files` to pull metadata and index it directly into your vector store.

Yes, you can use the allowed tools filter in LlamaIndex to restrict your agent's access. For instance, you can limit the agent to `quick_file_audit` and block access to detailed account metadata.

Only if you explicitly write them to a persistent vector index. By default, the LlamaIndex agent queries the live API via `get_file_access_statistics` to answer questions, keeping the data fresh and un-cached.

Your agent runs `list_expired_secure_files` to identify dead documents and clean up your index. This ensures your RAG application doesn't recommend or reference files that are no longer accessible to users.

All communication with the MCP Server runs through a zero-trust, ephemeral sandbox. This architecture guarantees that sensitive member lists and file permissions are never exposed to external servers or logged in transit.

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