Google Roads MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Google Roads as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Google Roads. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Google Roads?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Google Roads tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Google Roads MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Google Roads MCP Server
LlamaIndex provides unique advantages when paired with Google Roads through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Google Roads tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Google Roads tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Google Roads, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Google Roads tools were called, what data was returned, and how it influenced the final answer
Google Roads + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Google Roads MCP Server delivers measurable value.
Hybrid search: combine Google Roads real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Google Roads to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Google Roads for fresh data
Analytical workflows: chain Google Roads queries with LlamaIndex's data connectors to build multi-source analytical reports
Google Roads MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Google Roads to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Google Roads to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGoogle Roads + LlamaIndex FAQ
Common questions about integrating Google Roads MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Google Roads with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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.
Connect Google Roads to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.