How to Use the Bookeo MCP in LangChain

Build complex booking chains in LangChain by chaining Bookeo tool calls into multi-step agent workflows.

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Connect Bookeo MCP to LangChain

Create your Vinkius account to connect Bookeo 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 Bookeo with LangChain

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ChatGPT

Claude

Perplexity

Chain Bookeo tool outputs in LangChain

Feed `list_bookings` output directly into subsequent logic to calculate revenue or manage schedules. Your LangChain agent handles the flow, treating every tool call as a discrete step in a larger reasoning chain. This keeps your application logic clean. You define the sequence, and the agent executes the path without manual intervention.

Automated schedule management

Use `check_availability` to feed real-time slot data into your LangGraph nodes. The server returns raw JSON that your chain parses to make immediate booking decisions. This replaces hard-coded scheduling logic. Your agent evaluates the data and proceeds to the next chain link based on actual business capacity.

Traceable booking pipelines

Monitor every `get_customer` call through LangSmith to see exactly what data entered the agent. You get full visibility into the input-output mapping for every step. Debugging becomes a matter of checking the trace logs. You see where the chain stalled or why a specific customer profile was requested.

Setup guide

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

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Common questions about Bookeo MCP in LangChain

Pass the tool outputs into a shared state object within your LangGraph workflow. This allows the agent to reference past booking results during later steps in the chain.

Yes, configure your agent with the necessary tools and define a condition that triggers them. It will execute the action whenever the agent's logic determines a booking is required.

The server provides consistent schema definitions that match your account data. Your agent receives structured inputs every time, which prevents errors during high-volume processing.

The agent receives an error response, which you can catch using standard error handling nodes. You choose whether to retry the call or stop the execution chain.

Your customer profiles remain within your local execution environment or the memory state you define. We enforce strict isolation so that no external entity gains access to your private scheduling information.

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