How to Use the 4D MCP in LangChain

Q: How do I connect the 4D MCP Server to LangChain?

Install langchain-mcp-adapters in your project. Then initialize a MultiServerMCPClient pointing to your Vinkius endpoint URL.

Q: Can LangChain agents update 4D records autonomously?

Yes. ReAct agents will format the JSON payload and execute updateentity when their reasoning chain determines a record needs changing. You control the permissions via the tool bindings.

Connect your 4D database straight into LangChain ReAct agents to run ORDA queries and manage records automatically.

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Connect 4D MCP to LangChain

Create your Vinkius account to connect 4D to LangChain 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 4D with LangChain

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Map 4D Schema Inside LangChain

The `get_catalog` tool pulls your entire 4D database structure directly into your LangChain agent's context. Your ReAct agent reads the available DataClasses and figures out exactly how your tables relate before attempting any operations. Once the schema is mapped, the agent uses `get_entity` to pull specific records by primary key. LangSmith tracks the exact token usage and latency for every database fetch, giving you full visibility into how your chain interacts with the MCP Server.

Feed 4D Data to ReAct Pipelines

The `list_entities` tool accepts ORDA-style parameters like $filter and $orderby to run precise database lookups. Your LangChain agent can construct complex queries based on intermediate results from previous chain steps. Taking the output of a vector search, your pipeline might pass those specific IDs right into this MCP tool. The fetched 4D records then become the exact input needed for the next reasoning step in your multi-tool chain.

Write 4D Records via MCP Server

Your automated workflows actually modify database state using the `create_entity` and `update_entity` tools. Both require a valid JSON string payload that your agent formats based on the catalog definition it retrieved earlier. Erasing outdated information is just as straightforward with `delete_entity`. You build the logic, and LangChain executes the exact sequence of 4D operations required to keep your backend data accurate without human intervention.

Setup guide

Set up 4D MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes 4D tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "4d-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent 4D transactions"
    })
    print(result["messages"][-1].content)

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Common questions about 4D MCP in LangChain

Install `langchain-mcp-adapters` in your project. Then initialize a `MultiServerMCPClient` pointing to your Vinkius endpoint URL.

Yes. ReAct agents will format the JSON payload and execute `update_entity` when their reasoning chain determines a record needs changing. You control the permissions via the tool bindings.

The agent receives the error output from the tool. It will usually attempt to fix its ORDA syntax and retry the `list_entities` call automatically.

LangSmith handles the observability for your entire pipeline. You see the exact latency and token overhead for every single MCP tool invocation.

Vinkius runs the integration inside an ephemeral V8 Isolate Sandbox. Your proprietary DataClass structures and row-level text payloads pass directly to your AI client without being stored on our infrastructure.

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