Can my agent search for specific medical specialists in a city?

Yes. Use the 'rechercher_praticiens' tool. Provide the specialty (e.g., 'médecin généraliste') and the city. The agent will retrieve matching practitioner profiles from Doctolib's active directory natively.

How do I book a consultation slot through the agent?

First, use 'disponibilites' to find free slots for a doctor. Then, provide the practitioner ID, the chosen slot ID, and your email to the 'prendre_rendez_vous' tool to confirm your reservation in the database.

Can I see all medical practices connected to my account?

Absolutely. Use the 'lister_cabinets' tool. Your agent will perform a structural extraction of localized entity bounds, showing you all physical office locations managed within your Doctolib partner environment.

Doctolib MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: Doctolib
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Doctolib through Vinkius, pass the Edge URL in the `mcps` parameter and every Doctolib tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Doctolib Specialist",
    goal="Help users interact with Doctolib effectively",
    backstory=(
        "You are an expert at leveraging Doctolib tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Doctolib "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* 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.

When paired with CrewAI, Doctolib becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Doctolib tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Doctolib MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from Doctolib

Why Use CrewAI with the Doctolib MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Doctolib through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Doctolib + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Doctolib MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Doctolib for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Doctolib, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Doctolib tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Doctolib against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Doctolib MCP Tools for CrewAI (8)

These 8 tools become available when you connect Doctolib to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Doctolib to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Doctolib + CrewAI FAQ

Common questions about integrating Doctolib MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.