Doctolib MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Doctolib through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"doctolib": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Doctolib, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Doctolib MCP Server
Connect your Doctolib partner account to any AI agent and take full control of your healthcare scheduling and practitioner research through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Doctolib through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Practitioner Discovery — Search for doctors and specialists by specialty and city, identifying bounded office locations and member approximations natively
- Availability Tracking — Identify bounded routing spaces verifying absolute time availability slots attached directly matching the targeted doctor
- Appointment Management — List complex mappings evaluating exactly scheduled times and identifying physical reservations active within your account
- Live Booking — Commands the backend orchestrating real-time database locks inserting explicit reservation parameters structurally binding to an exact time slot
- Visit Motive Identification — Read available reason categories explicitly supported by a given Practitioner required for slot lock verification
- Practice Navigation — Perform structural extraction of localized entity bounds configuring the raw office locations active within the application
- Specialty Mapping — Enumerate explicitly attached structured roles defining valid medical specialties and practitioner targets globally
The Doctolib MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Doctolib to LangChain via MCP
Follow these steps to integrate the Doctolib MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from Doctolib via MCP
Why Use LangChain with the Doctolib MCP Server
LangChain provides unique advantages when paired with Doctolib through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Doctolib MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Doctolib queries for multi-turn workflows
Doctolib + LangChain Use Cases
Practical scenarios where LangChain combined with the Doctolib MCP Server delivers measurable value.
RAG with live data: combine Doctolib tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Doctolib, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Doctolib tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Doctolib tool call, measure latency, and optimize your agent's performance
Doctolib MCP Tools for LangChain (8)
These 8 tools become available when you connect Doctolib to LangChain via MCP:
consulter_praticien
Consulter le profil d'un praticien
disponibilites
Vérifier les créneaux disponibles pour un praticien
lister_cabinets
Lister les cabinets médicaux
lister_rendez_vous
Lister les rendez-vous pris
lister_specialites
Lister toutes les spécialités médicales disponibles
motifs_consultation
Lister les motifs de consultation d'un praticien
prendre_rendez_vous
Prendre un rendez-vous médical
rechercher_praticiens
Restricts search to explicit city boundaries natively bypassing local lists. Rechercher des praticiens par spécialité et ville
Example Prompts for Doctolib in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Doctolib immediately.
"Search for general practitioners in Paris"
"What are the available slots for Dr. Martin (ID: 123) tomorrow?"
"List my upcoming medical appointments"
Troubleshooting Doctolib MCP Server with LangChain
Common issues when connecting Doctolib to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersDoctolib + LangChain FAQ
Common questions about integrating Doctolib MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Doctolib with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Doctolib to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.