How to Use the WorkflowMax MCP in LangChain

Q: Can WorkflowMax and LangChain track client contacts?

Absolutely. You can use listclients to find an ID, then pass that ID into getclientcontact. This lets your AI client gather all necessary contact details for a specific client in a single, reliable sequence.

Q: What is the best way to manage multiple projects with WorkflowMax and LangChain?

Use the agent's ability to chain. You can run listjobs to see everything, then loop through those IDs, calling getclient for each one to get a full project overview automatically.

Build multi-step workflows for WorkflowMax using LangChain.

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Connect WorkflowMax MCP to LangChain

Create your Vinkius account to connect WorkflowMax 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 WorkflowMax with LangChain

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Automated Client Setup via MCP Server

The `create_client` tool initiates the process by establishing a new company or individual in your system. You'll use this output ID immediately to call `create_client_contact`, ensuring every client record has necessary contact details attached. This sequence allows your AI agent to handle multi-step data entry automatically. It moves beyond simple single calls, building complex business logic like setting up a new client and their primary contacts in one chain.

Managing Job Lifecycle with LangChain

When you need project oversight, the `list_jobs` tool gives you an overview of all active projects. From there, your agent can select a specific job UUID and call `update_job` to modify status or details. This capability allows for programmatic maintenance of records. Furthermore, if a job needs more detail, calling `get_job` retrieves the full record. The chain then uses that data to decide which next step is necessary, like generating a list of available tasks using `list_tasks`.

Time Tracking and Auditing with WorkflowMax

Need to track time? First, you call `create_timesheet` with the job UUID, task UUID, and hours worked. This action records the work effort directly against a project. Later, if someone needs an audit trail, running `list_timesheets` gives you all historical logs. The agent can then filter this data using `list_tasks`, proving exactly where the time was spent for any given client.

Setup guide

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

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Common questions about WorkflowMax MCP in LangChain

LangChain excels at sequencing. You can chain together calls—for instance, starting with `list_clients`, then getting a specific job using `get_job`, and finally pulling associated timesheets. The agent manages the flow from one tool output to the next.

Absolutely. You can use `list_clients` to find an ID, then pass that ID into `get_client_contact`. This lets your AI client gather all necessary contact details for a specific client in a single, reliable sequence.

Use the agent's ability to chain. You can run `list_jobs` to see everything, then loop through those IDs, calling `get_client` for each one to get a full project overview automatically.

Yes. The MCP Server supports several update functions, notably `update_job`. This lets your agent modify job details without needing to recreate the entire record.

This server handles structured business data including Client IDs, Job UUIDs, Contact Details, and Timesheet records. It manages the core relational information for project billing.

Use it with your favorite AI tools

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