Doctolib MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Doctolib as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Doctolib. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Doctolib?"
)
print(response)
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.
LlamaIndex agents combine Doctolib tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Doctolib MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Doctolib
Why Use LlamaIndex with the Doctolib MCP Server
LlamaIndex provides unique advantages when paired with Doctolib through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Doctolib tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Doctolib tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Doctolib, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Doctolib tools were called, what data was returned, and how it influenced the final answer
Doctolib + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Doctolib MCP Server delivers measurable value.
Hybrid search: combine Doctolib real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Doctolib to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Doctolib for fresh data
Analytical workflows: chain Doctolib queries with LlamaIndex's data connectors to build multi-source analytical reports
Doctolib MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Doctolib to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Doctolib to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDoctolib + LlamaIndex FAQ
Common questions about integrating Doctolib MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.