How to Use the Doctolib MCP in LangChain

Q: How do I search for a doctor on Doctolib using LangChain?

You pass the rechercherpraticiens tool to your agent. Provide the city and specialty as arguments, and the agent returns the matching list directly in the chain.

Q: Can LangChain verify Doctolib availability before booking?

Yes. You chain the disponibilites tool first to check open slots. Once the agent confirms a slot, it triggers prendrerendezvous to finalize the booking.

Chain Doctolib medical data directly into your LangChain agents for automated scheduling and practitioner lookups.

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Connect Doctolib MCP to LangChain

Create your Vinkius account to connect Doctolib 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 Doctolib with LangChain

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Automate appointment flows in LangChain

Chain `rechercher_praticiens` and `disponibilites` to build automated booking pipelines. Your LangChain agent handles the logic, pulling real-time availability into your custom workflows. Everything happens in sequence. You define the agent's intent, and the MCP server executes the calls while you track every step through LangSmith.

Dynamic practitioner lookups

Use `consulter_praticien` to pull detailed profiles based on previous search results. The agent filters candidates based on specialty or city without manual intervention. This keeps your data flow tight. By connecting `lister_specialites` to your prompt logic, you ensure the agent only queries relevant medical fields.

Integrated medical management

Manage your schedule by chaining `lister_rendez_vous` with `prendre_rendez_vous`. The agent validates existing commitments before attempting new bookings. This setup prevents conflicts. You get a clear view of your calendar, with the MCP server handling the underlying API interactions behind the scenes.

Setup guide

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

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Common questions about Doctolib MCP in LangChain

You pass the `rechercher_praticiens` tool to your agent. Provide the city and specialty as arguments, and the agent returns the matching list directly in the chain.

Yes. You chain the `disponibilites` tool first to check open slots. Once the agent confirms a slot, it triggers `prendre_rendez_vous` to finalize the booking.

It does. You can register these tools across multiple LangChain agents, letting them coordinate searching and booking tasks based on your specific chain logic.

The server acts as a thin bridge to the API. It only processes the specific appointment parameters you send, keeping sensitive health data within the session scope.

No. You provide an endpoint token to the Vinkius-hosted server. The server manages the connection, and your LangChain agent never touches raw credentials.

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