How to Use the Aero Workflow MCP in LangChain

Chain Aero Workflow tasks directly into your LangChain agents to automate accounting firm operations without manual data entry.

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Connect Aero Workflow MCP to LangChain

Create your Vinkius account to connect Aero Workflow 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 Aero Workflow with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Sequence Aero Workflow tasks in LangChain

Feed the output of `list_firm_customers` directly into subsequent chain operations. You build pipelines where your agent pulls raw client data and immediately formats it for internal reporting. Your agent handles the logic between steps. It takes `get_task_details` output to decide if a file needs an update or a new status flag.

Trace Aero Workflow logic with LangSmith

Monitor every tool call as it happens. You see exactly what `create_new_customer` sent and how the API responded in real-time. Debugging becomes trivial when you trace the agent's decision path. You catch errors in `create_new_workflow_task` before they hit your production database.

Aggregate multiple MCP servers

Connect this MCP Server alongside other data sources in your LangChain graph. Your agent correlates time entries from `list_time_tracking_logs` with external calendar data. This setup removes the need for custom glue code. Your agent manages the state between different services using standard interface calls.

Setup guide

Set up Aero Workflow 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 Aero Workflow 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({
    "aero-workflow-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 Aero Workflow transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Aero Workflow MCP in LangChain

Use the MultiServerMCPClient to initialize the connection. Once you call get_tools, pass them directly into your agent constructor to start building sequences.

Yes, use client.session() to maintain context. This keeps your agent aware of previous tool outputs while performing multi-step accounting tasks.

LangSmith logs every input and output for your Aero Workflow calls. You get full visibility into why a specific task failed or returned unexpected data.

No, you can define complex ReAct loops. The agent evaluates the output of `get_account_info` and decides whether to trigger further actions.

Your firm's customer records and task metadata remain isolated. Only the specific endpoints exposed by the server interact with your agent's memory.

Use it with your favorite AI tools

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