How to Use the Google Roads MCP in Pydantic AI

Get type-safe road matching and speed limit data for Pydantic AI agents with strict runtime validation.

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Connect Google Roads MCP to Pydantic AI

Create your Vinkius account to connect Google Roads 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 Google Roads with Pydantic AI

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Type-safe route snapping

The `snap_to_roads` tool returns structured data that validates against your Pydantic models. You won't deal with silent errors or malformed payloads. Your Pydantic AI agent will catch any unexpected response format immediately. This keeps your routing logic predictable and stable.

Validate speed limits in Pydantic AI

Use `get_snapped_speed_limits` to get validated speed data. Every response is checked against your expected schema before the agent processes it. This prevents the agent from hallucinating or using invalid speed values. You get clean, verified data for every road segment queried.

Perform road segment lookups

Call `get_nearest_roads` to identify road identities for specific locations. The output is strictly typed, ensuring your Pydantic AI agents receive exactly what they expect. This approach stops corruption in your data pipeline. It is the safest way to handle individual coordinate lookups in a production environment.

Setup guide

Set up Google Roads 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": {
        "google-roads-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Google Roads 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 Google Roads. 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 Google Roads MCP in Pydantic AI

The MCP server response is automatically validated against your defined Pydantic models. If the data structure is wrong, the agent stops execution.

You can. The tools return speed values that are validated at runtime, ensuring your agent logic relies on accurate regulatory data.

It does. You define the toolset using the provided server URL, and Pydantic AI handles the type-safe integration for you.

You provide an endpoint token to the Vinkius-managed server. This keeps your credentials out of your local agent code.

It processes raw GPS coordinates and returns road-snapped metadata. The server is strictly limited to these geographic calculations.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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