Google Roads MCP Server
Access Google Roads API — snap GPS tracks to roads, find nearest road segments, and get speed limit data for any road segment worldwide from any AI agent.
Ask AI about this MCP Server
Vinkius supports streamable HTTP and SSE.
* 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
What is the Google Roads MCP Server?
The Google Roads MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Google Roads via 4 tools. Access Google Roads API — snap GPS tracks to roads, find nearest road segments, and get speed limit data for any road segment worldwide from any AI agent. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (4)
Tools for your AI Agents to operate Google Roads
Ask your AI agent "Snap these GPS coordinates to roads: 40.7128,-74.0060|40.7135,-74.0055|40.7142,-74.0048" and get the answer without opening a single dashboard. With 4 tools connected to real Google Roads data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Google Roads MCP Server capabilities
4 toolsReturns 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
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
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
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
What the Google Roads MCP Server unlocks
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.
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
How it works
1. Subscribe to this server
2. Enter your Google Maps Platform API key with Roads API enabled
3. Start matching GPS data to roads from Claude, Cursor, or any MCP-compatible client
No more manual map matching or noisy GPS data visualization. Your AI acts as a dedicated GPS data analyst and road matching assistant.
Who is this for?
- Fleet Managers — clean GPS tracks from vehicles, identify roads travelled, and monitor speed compliance
- Mapping Developers — convert raw GPS traces into clean road geometries for visualization and analysis
- Safety Analysts — retrieve speed limit data for road segments to analyze driver behavior and compliance
- GIS Professionals — snap scattered GPS points to road networks for spatial analysis and cartography
Frequently asked questions about the Google Roads MCP Server
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.
More in this category
LinkedIn Page Management
7 toolsManage Company Page posts, comments, and social actions via the LinkedIn REST API.
CFPB Complaints
9 toolsSearch 13.8M+ consumer complaints against financial companies — filter by product, company, state and issue.
HubSpot Service Hub
6 toolsManage support tickets, track pipelines, and view customer feedback through natural conversation.
You might also like
Evisort Contract Intelligence
10 toolsEquip your AI agent to manage contract documents, track AI-extracted provisions, and monitor workflows via the Evisort API.
Telekom Malaysia (TM) OIP
10 toolsOrchestrate Telekom Malaysia (TM) services — manage broadband plans, service orders, and network usage directly from any AI agent.
Open-Meteo Flood & Rivers
3 toolsPredict floods before they happen: GloFAS river discharge simulations at 5km, 40 years of historical data (1984–present), and 7-month forward forecasts for disaster prevention.
Connect Google Roads with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Give your AI agents the power of Google Roads MCP Server
Production-grade Google Roads MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.