How to Use the 4D MCP in LlamaIndex

Q: How do I configure LlamaIndex to use the 4D MCP Server?

Run pip install llama-index-tools-mcp. Setup a BasicMCPClient with your endpoint URL and wrap it in McpToolSpec before passing to your FunctionAgent.

Index your 4D DataClasses directly into LlamaIndex to build RAG applications grounded in live database records.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect 4D MCP to LlamaIndex

Create your Vinkius account to connect 4D 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.

GDPR Free for Subscribers

Setup 4D with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index 4D DataClasses with LlamaIndex

The `get_catalog` tool grabs your complete database definition so LlamaIndex can embed your schema structure into its vector store. Your RAG application now understands the exact relationships between your tables before it ever runs a query. This semantic mapping means user questions map instantly to the correct DataClass. The agent knows exactly where to look for specific entity types without guessing table names or columns.

Feed Vector Stores via MCP Server

LlamaIndex uses `list_entities` to pull batches of records using ORDA filters and orderings. These results get indexed immediately, turning raw relational data into searchable semantic nodes. For single-record retrieval, `get_entity` fetches exact matches by primary key. Your AI client combines these live database pulls with your static documents to generate answers grounded in actual, current system state.

Mutate Records from RAG Workflows

RAG apps don't have to be read-only when you equip them with `create_entity` and `update_entity`. Your agent constructs the required JSON string payloads based on information it synthesizes from user prompts and existing indexes. Managing stale data is handled through the `delete_entity` tool. The MCP setup lets your knowledge-augmented systems actively maintain the 4D database rather than just reporting on it.

Setup guide

Set up 4D 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 4D 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 4D tools.",
)
response = await agent.run("List recent 4D data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by 4D. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect 4D now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about 4D MCP in LlamaIndex

Run `pip install llama-index-tools-mcp`. Setup a `BasicMCPClient` with your endpoint URL and wrap it in `McpToolSpec` before passing to your FunctionAgent.

It pulls records using the available query tools and indexes the output. You use ORDA syntax in the tool call to fetch the relevant subset of data first.

The catalog provides the blueprint. Your agent reads the schema and structures the payload correctly for any write operations.

You gain semantic search capabilities over relational data. The framework embeds your database records alongside PDFs and text files for unified querying.

The connection operates through a zero-trust sandbox architecture. Your raw JSON payloads and schema definitions route securely to your agent and are never persisted by Vinkius.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript