Doctolib MCP Server for Pydantic AI 8 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Doctolib through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Doctolib "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Doctolib?"
)
print(result.data)
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.
Pydantic AI validates every Doctolib tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Doctolib MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the Doctolib MCP Server
Pydantic AI provides unique advantages when paired with Doctolib through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Doctolib integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Doctolib connection logic from agent behavior for testable, maintainable code
Doctolib + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Doctolib MCP Server delivers measurable value.
Type-safe data pipelines: query Doctolib with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Doctolib tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Doctolib and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Doctolib responses and write comprehensive agent tests
Doctolib MCP Tools for Pydantic AI (8)
These 8 tools become available when you connect Doctolib to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Doctolib to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiDoctolib + Pydantic AI FAQ
Common questions about integrating Doctolib MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.