How to Use the Doctolib MCP in Pydantic AI

Q: How do I configure Doctolib with Pydantic AI?

Install the slim MCP package and use the MCPToolset constructor with your server URL. Pydantic AI automatically maps the server tools to your agent's function calls.

Build type-safe medical booking agents with Pydantic AI and the Doctolib MCP Server for reliable, validated appointment data.

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Connect Doctolib MCP to Pydantic AI

Create your Vinkius account to connect Doctolib to Pydantic AI 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 Pydantic AI

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Perplexity

Type-safe practitioner lookup for Pydantic AI

Use `rechercher_praticiens` to get search results that validate against your Pydantic schemas. If the API returns unexpected fields, the agent stops immediately. Query `consulter_praticien` to pull detailed profiles into your typed agent models. This enforces strict structure on the data the agent uses to evaluate a doctor.

Validated booking flows in Pydantic AI

Fetch slots using `disponibilites` and verify them against your internal types. This prevents the agent from hallucinating slot times that don't exist. Call `prendre_rendez_vous` to commit the appointment once the data passes validation. Your Pydantic models confirm the response is valid before your agent confirms success.

Structured consultation handling in Pydantic AI

Request valid consultation reasons with `motifs_consultation` to ensure compliance. The agent checks this against your schema before submitting a booking request. Retrieve current bookings via `lister_rendez_vous` as a typed list. This allows your agent to process existing appointments without risking type mismatch errors.

Setup guide

Set up Doctolib MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "doctolib-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Doctolib tools.",
)

result = await agent.run("List recent Doctolib transactions")
print(result.output)

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Common questions about Doctolib MCP in Pydantic AI

Install the slim MCP package and use the `MCPToolset` constructor with your server URL. Pydantic AI automatically maps the server tools to your agent's function calls.

The agent will raise a validation error immediately. This stops the execution flow, preventing your agent from processing invalid appointment data or corrupting your database.

It is. You can specify the SSE transport in your toolset configuration to maintain a persistent connection between your agent and the Doctolib API.

Yes, because the server is model-agnostic. You can pair it with any local model as long as you provide the Pydantic schemas for the tool inputs and outputs.

Identifiers are treated as opaque strings. The server never inspects or logs the patient's internal Doctolib ID, ensuring that your agent logic remains the sole handler of that sensitive value.

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