How to Use the Exact Online Bouw MCP in LangChain

Q: How do I map WBS nodes in a LangChain agent?

You use the listprojectwbs tool to fetch the structure into the agent's context. The agent then uses this data to make decisions about which transaction to fire next.

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Connect Exact Online Bouw MCP to LangChain

Create your Vinkius account to connect Exact Online Bouw 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 Exact Online Bouw with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate project cost workflows

Construct execution chains that trigger `create_cost_transaction` based on external events. Agents parse incoming invoices and post them directly to the correct project ledger without human intervention. This removes the manual data entry bottleneck. You define the sequence where raw data feeds into your agent, ensuring every cost is accounted for immediately.

Dynamic hour tracking pipelines

Connect `create_time_transaction` to your LangGraph logic. Agents verify logged hours against `list_project_hour_budgets` before submitting entries to the system. If the agent detects an overage, it flags the discrepancy instantly. Your pipeline handles the logic while the MCP server manages the API handshake.

Sync WBS and project data

Use `list_project_wbs` to fetch live structures for your agents. LangChain agents iterate through these nodes to map tasks to current budget availability. This creates a real-time view of your project health. You get accurate, actionable data flowing through your chain every time the agent runs.

Setup guide

Set up Exact Online Bouw 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 Exact Online Bouw 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({
    "exact-online-bouw-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 Exact Online Bouw transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Exact Online Bouw MCP in LangChain

You use the MCP adapter to expose server tools to your agent. This lets your chain call functions directly as part of its execution path.

Yes. You build queuing logic into your LangGraph nodes to manage the call volume. This ensures your agent stays within the API limits while processing large batches.

Your data stays within the encrypted pipe between the server and the agent. The system doesn't store credentials locally, keeping your project records private.

You use the `list_project_wbs` tool to fetch the structure into the agent's context. The agent then uses this data to make decisions about which transaction to fire next.

The MCP server returns an error to your agent. You can then configure your LangChain logic to retry the operation or alert a human team member.

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