Can my AI snap a GPS track to the actual roads travelled?

Yes! Use the `snap_to_roads` tool with your GPS coordinates in path format (latitude,longitude pairs separated by pipes). For example: path=40.7128,-74.0060|40.7135,-74.0055|40.7142,-74.0048. Set interpolate=true for smoother road geometry with additional interpolated points between your input coordinates. The response includes snapped coordinates, original coordinates, and place IDs for each road segment.

How do I get speed limit data for a specific road segment?

Use the `get_speed_limits` tool with place IDs obtained from snap_to_roads or get_nearest_roads responses. For example: place_ids=ChIJplaceId1|ChIJplaceId2|ChIJplaceId3. The API returns speed limits in km/h for each road segment. If you need both snapped coordinates AND speed limits in one call, use `get_snapped_speed_limits` with a GPS path instead.

What is the difference between snap_to_roads and get_nearest_roads?

snap_to_roads assumes your coordinates form a continuous path and snaps them to the most likely sequence of roads travelled, with optional interpolation for smoother geometry. get_nearest_roads treats each coordinate independently and finds the nearest road segment for each point without assuming they form a path. Use snap_to_roads for GPS tracks and routes, and get_nearest_roads for scattered individual points.

Google Roads MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: Google Roads
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Google Roads through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

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 Google Roads "
            "(4 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Google Roads?"
    )
    print(result.data)

asyncio.run(main())

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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 Google Roads MCP Server

Connect your Google Roads API to any AI agent and take full control of GPS map matching, road segment identification, and speed limit data retrieval through natural conversation.

Pydantic AI validates every Google Roads tool response against typed schemas, catching data inconsistencies at build time. Connect 4 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

Snap to Roads — Match GPS coordinate paths to the most likely roads travelled with interpolated points for smooth road geometry
Nearest Roads — Find the nearest road segment for up to 100 individual GPS coordinates independently
Speed Limits — Get posted speed limit data for specific road segments using place IDs from road matching
Snapped Speed Limits — Snap GPS coordinates to roads AND get speed limits in a single combined request
Place ID Mapping — Obtain Google place IDs for road segments that can be used with other Google Maps APIs
Fleet Tracking — Clean noisy GPS traces from fleet vehicles for accurate route visualization
GPS Correction — Convert raw GPS points into accurate road-level positions for mapping applications

The Google Roads MCP Server exposes 4 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 Google Roads to Pydantic AI via MCP

Follow these steps to integrate the Google Roads 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 4 tools from Google Roads with type-safe schemas

Why Use Pydantic AI with the Google Roads MCP Server

Pydantic AI provides unique advantages when paired with Google Roads 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 Google Roads integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Google Roads connection logic from agent behavior for testable, maintainable code

Google Roads + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Google Roads MCP Server delivers measurable value.

Type-safe data pipelines: query Google Roads with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Google Roads tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Google Roads and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Google Roads responses and write comprehensive agent tests

Google Roads MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect Google Roads to Pydantic AI via MCP:

get_nearest_roads

Returns the snapped coordinate, the original coordinate, and the place ID for each nearest road segment. Unlike snapToRoads which assumes coordinates form a continuous path, nearestRoads treats each point independently. Essential for reverse geocoding, finding which road a vehicle is on, identifying road segments for individual location points, and mapping scattered GPS points to roads. Each point is matched to the nearest road segment within a reasonable distance. Place IDs can be used with the speed limits endpoint. AI agents should reference this when users ask "what road is at these coordinates", "find the nearest road for each GPS point", or need to map individual location points to road segments without assuming a path. Get the nearest road segments for up to 100 individual GPS coordinates

get_snapped_speed_limits

Snaps GPS coordinates to the nearest road segments and returns both the snapped coordinates with place IDs AND the speed limits for each road segment. This is more efficient than making separate calls to snapToRoads and then speedLimits. Returns snapped points with place IDs, original coordinates, and speed limit data in km/h for each road segment. Essential for applications that need both map-matched road geometry and speed limit data, such as fleet management, driver safety monitoring, route planning with speed awareness, and GPS track analysis. AI agents should reference this when users ask "snap these GPS points to roads and show speed limits", "get both snapped coordinates and speed limits for this route", or need combined road matching and speed limit data in one call. Snap GPS coordinates to roads and get speed limits in a single request

get_speed_limits

Returns speed limit values in km/h along with the place IDs and corresponding road segment information. Place IDs are obtained from the snapToRoads or nearestRoads responses. Essential for speed compliance monitoring, fleet safety management, driver behavior analysis, and road safety applications. Speed limits reflect posted legal limits and may vary by road type, urban/rural designation, and local regulations. AI agents should use this when users ask "what is the speed limit on this road segment", "get speed limits for these place IDs", or need speed limit data for specific road segments identified through map matching. Get speed limit data for specific road segments using place IDs

snap_to_roads

Returns snapped coordinates with place IDs, original coordinates, and interpolated points along the road. Essential for map matching, GPS track correction, route reconstruction, fleet tracking visualization, and converting raw GPS traces into clean road geometries. The path parameter accepts up to 100 coordinate pairs in "lat,lng|lat,lng" format. Set interpolate=true to return additional points between input coordinates for smoother road geometry. Place IDs returned can be used with the speed limits endpoint to get speed limit data for each road segment. AI agents should use this when users ask "snap this GPS track to roads", "match these coordinates to the actual roads travelled", or need to clean up noisy GPS data for mapping and visualization. Snap GPS coordinates to the most likely roads travelled using Google Roads API

Example Prompts for Google Roads in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Google Roads immediately.

"Snap these GPS coordinates to roads: 40.7128,-74.0060|40.7135,-74.0055|40.7142,-74.0048"

"Get speed limits for these place IDs: ChIJd8BlQ2BZwokRAFUEcm_qrcA|ChIJd8BlQ2BZwokRAFUEcm_qrcB"

"Find the nearest road to these coordinates: 34.0522,-118.2437 and 34.0530,-118.2445"

Troubleshooting Google Roads MCP Server with Pydantic AI

Common issues when connecting Google Roads to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Google Roads + Pydantic AI FAQ

Common questions about integrating Google Roads MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an 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?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Google Roads MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.