How to Use the Bexio MCP in LangChain

Q: How do I connect the Bexio MCP Server to my LangChain agent?

Install the langchain-mcp-adapters package and use MultiServerMCPClient to point to the Vinkius endpoint. From there, call gettools() and pass them directly into your agent constructor to start querying Swiss ERP data.

Q: Can LangChain execute multi-step operations in Bexio?

Yes. A ReAct agent can inspect active work using listprojects, look up the project owner with getcontact, and then draft a new record using createcontact in a single execution run.

Q: How does LangChain handle tool outputs from this Bexio integration?

The adapter converts the JSON payloads from listinvoices or listorders into standard LangChain tool messages. Your agents can read these messages directly to make decisions or pass them to another prompt template.

Feed live Bexio ERP data straight into your LangChain multi-step reasoning chains with this dedicated MCP Server.

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Connect Bexio MCP to LangChain

Create your Vinkius account to connect Bexio 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 Bexio with LangChain

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Chain Bexio data directly into agent workflows

Your LangChain agent can grab a customer profile using `get_contact` and immediately feed those details into the next step of your chain. This lets you build multi-step pipelines where the output of one ERP query directly determines what the agent does next without manual glue code. For instance, you can design a ReAct loop that pulls unpaid bills via `list_invoices` and automatically matches them against projects found through `list_projects`. The agent handles the logical progression, deciding which tool to call based on the live data it pulls.

Trace and debug Bexio MCP Server queries in LangSmith

Every time your agent triggers `list_items` or checks user lists with `list_users`, LangSmith logs the exact payload. You can watch the latency, track token usage, and inspect the raw JSON inputs and outputs of your Swiss ERP calls in real time. This observability stops silent failures in their tracks. When debugging complex multi-agent chains that create contacts via `create_contact`, you see exactly why a call failed or where the chain slowed down.

Aggregate Swiss business data with other APIs

Combining tools is where LangChain shines. You can pair this MCP Server with database or vector store tools in a single `MultiServerMCPClient` setup to build cross-platform automation. An agent can retrieve client context from your CRM, pull corresponding sales records using `list_offers` or `list_orders`, and compile a unified report. The adapter handles the tool schemas so your code stays clean.

Setup guide

Set up Bexio 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 Bexio 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({
    "bexio-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 Bexio 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 Bexio. 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 Bexio MCP in LangChain

Install the `langchain-mcp-adapters` package and use `MultiServerMCPClient` to point to the Vinkius endpoint. From there, call `get_tools()` and pass them directly into your agent constructor to start querying Swiss ERP data.

Yes. A ReAct agent can inspect active work using `list_projects`, look up the project owner with `get_contact`, and then draft a new record using `create_contact` in a single execution run.

The adapter converts the JSON payloads from `list_invoices` or `list_orders` into standard LangChain tool messages. Your agents can read these messages directly to make decisions or pass them to another prompt template.

Absolutely. You can bind the tools to a stateful graph node, allowing your custom agents to query `list_items` or update Swiss customer profiles during specific state transitions.

Your credentials and sensitive financial records, including invoices and contact lists, stay protected inside the secure Vinkius V8 sandbox. The system never stores your API tokens, and all data transfers run over encrypted, ephemeral tunnels that close as soon as your agent finishes its run.

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