How to Use the Doctolib MCP in AutoGen

Q: Can AutoGen agents use Doctolib to book appointments?

Yes. You provide the prendrerendezvous tool to your AssistantAgent. The agent can then trigger the booking as part of its collaborative workflow.

Q: How do AutoGen agents share Doctolib search results?

They pass the results from rechercherpraticiens between each other in the conversation history. This allows the team to refine the search based on shared context.

Let your AutoGen agents debate and execute Doctolib appointments with multi-agent consensus.

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Connect Doctolib MCP to AutoGen

Create your Vinkius account to connect Doctolib to AutoGen 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 AutoGen

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Debate booking decisions in AutoGen

Assign `prendre_rendez_vous` to a specialized agent while another reviews the `motifs_consultation`. The agents negotiate the correct appointment type before execution. This prevents errors. By forcing a debate, you ensure the agent selects the right consultation motive based on the clinic's requirements.

Collaborative practitioner search

Use `rechercher_praticiens` within a multi-agent group. One agent searches for candidates, while another evaluates them using `consulter_praticien`. The agents share the workload. This collaborative approach leads to better decisions, especially when matching specific medical needs to practitioner profiles.

Sync medical schedules

Coordinate `lister_rendez_vous` across multiple agents to manage your calendar. They verify current bookings before proposing new ones. This keeps your schedule consistent. The agents act as a team, ensuring that no new appointment interferes with existing commitments found in the system.

Setup guide

Set up Doctolib MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Doctolib tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Doctolib_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Doctolib data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Doctolib_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Doctolib data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Doctolib MCP in AutoGen

Yes. You provide the `prendre_rendez_vous` tool to your AssistantAgent. The agent can then trigger the booking as part of its collaborative workflow.

They pass the results from `rechercher_praticiens` between each other in the conversation history. This allows the team to refine the search based on shared context.

It does. You define the tools in your agent configuration, and any agent in the chat can call them to gather information or perform actions.

The server uses a zero-trust architecture. Every tool call via the MCP server is scoped to your specific request, preventing unauthorized data exposure.

It accesses public practitioner information and your private appointment records. All interactions are strictly isolated within your Vinkius-managed environment.

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