How to Use the Exact Online Bouw MCP in LlamaIndex

Q: Can I search WBS nodes using LlamaIndex?

Yes. You ingest the output of listprojectwbs into your index. Your agents then search for specific tasks or budget lines by description.

Index your construction data in LlamaIndex. Make Exact Online Bouw records searchable for grounded RAG performance.

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Connect Exact Online Bouw MCP to LlamaIndex

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

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Perplexity

Searchable project history

Query your past entries by turning `list_cost_transactions` output into searchable vectors. You get answers grounded in your actual financial history instead of guesswork. This gives your agent a memory of every expense. You simply ask about previous project costs, and the index provides the precise data point.

Unified knowledge retrieval

Map `list_employees` and `list_accounts` data into a single searchable store. Your RAG application correlates personnel records with current project assignments. This allows for complex queries across your entire organization. You find the right person for a task based on their recorded activity in the system.

Grounded budget analysis

Index the results from `list_project_hour_budgets` to provide context for your agents. Your system knows exactly how much budget remains before it suggests a new transaction. This prevents budget overruns before they happen. Your agent evaluates the data in the index to guide project planning with real numbers.

Setup guide

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

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Common questions about Exact Online Bouw MCP in LlamaIndex

You convert the tool outputs into a vector index. This allows your agent to perform semantic searches across your project and financial data.

It indexes the results you retrieve from the tools. You control the refresh frequency to ensure your agent always works with current information.

Your project data is processed locally within your RAG pipeline. The server handles authentication, and the index acts as your private, local knowledge store.

Yes. You ingest the output of `list_project_wbs` into your index. Your agents then search for specific tasks or budget lines by description.

The server uses token-based auth to restrict access. Your index only contains the data you choose to ingest, keeping sensitive financial records under your control.

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