How to Use the BlaBlaCar MCP in Pydantic AI

Build type-safe travel agents with Pydantic AI and the BlaBlaCar MCP Server.

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Connect BlaBlaCar MCP to Pydantic AI

Create your Vinkius account to connect BlaBlaCar 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 BlaBlaCar with Pydantic AI

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Perplexity

Type-safe search in Pydantic AI

Every response from `search_trips_by_city` is validated against a Pydantic model at runtime. If the API returns a malformed price or missing field, your agent catches it immediately. This stops bad data from breaking your logic. You get clean, predictable input every time you call a tool.

Validate driver profiles

The `get_driver_profile` tool returns structured data that your Pydantic AI agent verifies instantly. You define the schema for what a valid driver profile looks like in your code. If the data doesn't match your requirements, the agent halts before taking any action. This adds a layer of safety to your ride booking process.

Access international trip data

Use `search_international_trips` to pull cross-border ride options into your agent. The response is immediately cast into your Python objects for safe handling. This ensures your agent works with validated types across every international route. You can trust the data flowing through your system.

Setup guide

Set up BlaBlaCar 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": {
        "blablacar-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BlaBlaCar. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about BlaBlaCar MCP in Pydantic AI

Use the MCPToolset class to connect to your Vinkius server URL. Add the toolset to your Agent instance to gain access to the full suite of ride-searching tools.

Yes, the framework checks the incoming data against your defined schemas. This prevents silent errors when the provider updates their API response format.

The agent will raise a validation or execution error based on your configuration. You can handle these exceptions to keep your agent running smoothly.

It is built for reliability and consistent performance. By using type-safe models, you ensure your production agent handles real-world data correctly.

Your agent interacts with the server over an encrypted channel. We treat your search parameters and trip results as transient data that is never stored or logged by our infrastructure.

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