How to Use the Simplicate MCP in LangChain

Build complex decision chains with LangChain: Automate Benelux professional services workflows.

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Connect Simplicate MCP to LangChain

Create your Vinkius account to connect Simplicate to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Simplicate with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Automating Multi-Step Workflows

You can build multi-step agents that decide which tool to call and in what order. For example, your agent first runs `list_crm_organizations` to get company IDs, then uses those results to feed into a call like `get_project_details`. This sequence allows the agent to perform complex actions without human intervention. The ability to chain these tools is huge. The output from one function—say, getting a list of all contacts using `list_crm_persons`—becomes the input for the next step, like checking their project status with `get_project_details`. It's pure reasoning built on your data.

Managing Client Data Chains

The LangChain framework lets you stitch together various pieces of client information. Need to know a person's history? Your agent can grab all contacts with `list_crm_persons`, then pull the project services they are linked to using `list_project_services`. This gives a full picture, not just isolated data points. This is critical for cross-referencing accounts. You might start by getting an organization profile via `get_my_organization_profile` and finish by checking all associated invoices with `list_invoices`, all in one run. It keeps the whole workflow connected.

Time Tracking & Project Reporting

You can create a full audit trail for project work using chained tools. First, your agent pulls all active projects via `list_projects`. Next, it gathers every time entry logged through `list_time_registrations` and then checks the associated service catalogue with `list_project_services`. This provides instant billing verification. If a client asks for an update on specific work, the chain can pull out all relevant details. It might take project IDs from `get_project_details`, cross-reference them against employee records using `list_employees`, and finally log or confirm hours with `log_time_registration`. The whole process is traceable.

Setup guide

Set up Simplicate 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 Simplicate 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({
    "simplicate-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 Simplicate transactions"
    })
    print(result["messages"][-1].content)

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

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Common questions about Simplicate MCP in LangChain

LangChain treats every tool call as a node in a graph. The output of one function immediately becomes the input for another, allowing your agent to perform multi-step reasoning. You don't just run tools; you build decision pipelines.

Absolutely. Your client can aggregate calls across different MCP servers or even external APIs within the same chain. It means your agent isn't limited to just our tools; it's part of a larger, connected system.

This server touches organization profiles, project details, sales opportunities, employee lists, invoices, time registrations, and contact information. All data access is governed by the secure MCP framework.

Yes. The tools are built around core functions for Dutch professional services firms—managing billing, projects, and clients. This structure helps automate processes that must comply with local regulations.

You can build automated reports by chaining tools together. For instance, listing all invoices (`list_invoices`) then cross-referencing them against project details (`get_project_details`) generates a powerful financial summary.

Use it with your favorite AI tools

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