4D MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add 4D as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to 4D. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in 4D?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About 4D MCP Server
Bridge your 4D Server with the world of AI Agents through the power of ORDA (Object Relational Data Architecture). This integration transforms your 4D database into an intelligent, queryable knowledge base, allowing your AI agent to explore structures and manage records through natural conversation. No more manual REST calls; your agent can now audit catalogs, run complex entity queries, and perform high-speed CRUD operations, ensuring your 4D data is always accessible and actionable within your AI workflows.
LlamaIndex agents combine 4D tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Database Exploration — Retrieve the full catalog of DataClasses (tables) and their attribute definitions (fields) to map your data structure.
- Advanced Querying — Perform complex data lookups using filters, ordering, and expansion of related entities with ORDA syntax.
- CRUD Operations — Create, read, update, and delete records across any exposed DataClass in your 4D environment.
- Metadata Insights — Check server information, version, and database structure on the fly to ensure system integrity.
- Structured Access — Interact with your data using the modern ORDA model, ensuring consistency, type safety, and security.
The 4D MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect 4D to LlamaIndex via MCP
Follow these steps to integrate the 4D MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from 4D
Why Use LlamaIndex with the 4D MCP Server
LlamaIndex provides unique advantages when paired with 4D through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine 4D tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain 4D tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query 4D, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what 4D tools were called, what data was returned, and how it influenced the final answer
4D + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the 4D MCP Server delivers measurable value.
Hybrid search: combine 4D real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query 4D to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying 4D for fresh data
Analytical workflows: chain 4D queries with LlamaIndex's data connectors to build multi-source analytical reports
4D MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect 4D to LlamaIndex via MCP:
create_entity
Requires a JSON string representation of the data payload. Create a new record in the database
delete_entity
Delete a record from the database
get_catalog
Retrieve the database catalog definition
get_entity
Get a specific record by primary key
list_entities
Supports ORDA-style query parameters like $filter and $orderby for advanced lookups. Query records from a specific DataClass (table)
update_entity
Requires a JSON string payload. Update an existing record in the database
Example Prompts for 4D in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with 4D immediately.
"Show me the first 5 records from the 'Invoices' table."
"What tables (DataClasses) are exposed in my 4D catalog?"
"Create a new record in the 'Customers' table for 'John Doe' with email 'john@example.com'."
Troubleshooting 4D MCP Server with LlamaIndex
Common issues when connecting 4D to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcp4D + LlamaIndex FAQ
Common questions about integrating 4D MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect 4D with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect 4D to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.