How to Use the Egnyte MCP in LlamaIndex

Q: Can LlamaIndex update the Egnyte index automatically when files change?

Yes, LlamaIndex can query listwebhooks to monitor change events on your files. When a webhook fires, the indexer pulls the modified file metadata and updates the vector store.

Q: How do I restrict LlamaIndex RAG queries to specific Egnyte groups?

You use listgroups to verify the user's group membership before executing the query engine. LlamaIndex then filters the vector index to only return files the user has permission to see.

Q: Does the LlamaIndex MCP Server integration support deep folder searches?

Yes, LlamaIndex uses searchfiles to look across your entire folder hierarchy. It retrieves matching files and parses them into nodes for your index, bypassing directory limits.

Q: What file metadata does LlamaIndex extract from Egnyte?

The query engine uses getfilemetadata to extract file sizes, creation dates, and owner details. This metadata is stored as node attributes to allow precise filtering during RAG runs.

Q: How does this LlamaIndex MCP integration protect my Egnyte file metadata and user lists?

Your metadata and user directories fetched via listusers are processed locally within your LlamaIndex runtime. The Vinkius sandbox prevents any storage data from being exposed to public networks or third-party servers.

Index Egnyte folders directly into LlamaIndex vector stores for grounded RAG and semantic file search.

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Connect Egnyte MCP to LlamaIndex

Create your Vinkius account to connect Egnyte 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 Egnyte with LlamaIndex

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Index Egnyte folder structures in LlamaIndex

`list_folder` pulls directory trees directly into LlamaIndex document structures using this MCP Server. Your RAG pipeline maps the folder hierarchy to understand the context of your files. This gives your agent a structural map of your storage before it queries content. The index updates dynamically. When files change, LlamaIndex queries the directory to find new items, keeping your semantic index synchronized with your actual storage. You don't waste tokens indexing static, unchanged directories.

Ground RAG queries using Egnyte MCP Server search

`search_files` allows your LlamaIndex query engine to retrieve files based on semantic queries and file metadata. Instead of searching blindly, the engine uses this tool to locate target documents in your storage. It grounds your LLM responses in real, verified files. LlamaIndex pairs the file metadata from `get_file_metadata` with the indexed content. This ensures that when the agent answers a question, it cites the exact file path, modification date, and size. It eliminates hallucinated sources.

Build compliance-aware indexes with LlamaIndex

`get_audit_logs` feeds security events directly into your LlamaIndex knowledge base to track data access trends. You can query the index to ask questions like who accessed the Q4 folder last week and get answers grounded in real audit logs. It turns raw logs into a conversational database. The MCP server helps the framework combine this log data with `list_users` to build a complete profile of user activity. It allows security teams to run natural language audits over complex access histories without writing SQL.

Setup guide

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

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Common questions about Egnyte MCP in LlamaIndex

Yes, LlamaIndex can query `list_webhooks` to monitor change events on your files. When a webhook fires, the indexer pulls the modified file metadata and updates the vector store.

You use `list_groups` to verify the user's group membership before executing the query engine. LlamaIndex then filters the vector index to only return files the user has permission to see.

Yes, LlamaIndex uses `search_files` to look across your entire folder hierarchy. It retrieves matching files and parses them into nodes for your index, bypassing directory limits.

The query engine uses `get_file_metadata` to extract file sizes, creation dates, and owner details. This metadata is stored as node attributes to allow precise filtering during RAG runs.

Your metadata and user directories fetched via `list_users` are processed locally within your LlamaIndex runtime. The Vinkius sandbox prevents any storage data from being exposed to public networks or third-party servers.

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