How to Use the Google Roads MCP in LlamaIndex

Q: Can I filter Google Roads data in LlamaIndex?

Yes. You can use the allowedtools filter to restrict which Google Roads functions the agent can access. This keeps your index clean and prevents the agent from calling expensive tools unnecessarily.

Index your Google Roads telemetry into LlamaIndex for semantic search and grounded fleet intelligence.

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Connect Google Roads MCP to LlamaIndex

Create your Vinkius account to connect Google Roads to LlamaIndex 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 LlamaIndex

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Perplexity

Searchable road geometry

Turn raw GPS traces into structured road data using `snap_to_roads`. LlamaIndex ingests these snapped paths and indexes them as part of your persistent knowledge base. You can query your history to ask where a vehicle traveled last week. The agent retrieves the map-matched path from the index rather than re-calculating coordinates every time.

Grounding queries in speed limits

Query your index for speed limit compliance using `get_speed_limits`. LlamaIndex grounds your agent's answers in real-world legal data retrieved via the MCP Server. Your agent searches past trips to find segments where drivers exceeded the posted limits. It provides specific, evidence-backed answers based on the indexed speed data.

Unified road knowledge

Combine `get_nearest_roads` outputs with your private documents. LlamaIndex creates a single queryable source that merges live Google Roads data with your internal policy files. Your agent can compare a vehicle's location against internal restricted zones. It pulls the road segment data and checks it against your uploaded safety documentation.

Setup guide

Set up Google Roads MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Google Roads MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Google Roads tools.",
)
response = await agent.run("List recent Google Roads data")

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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 LlamaIndex

LlamaIndex converts tool outputs into vector embeddings or structured nodes. This allows your agent to perform semantic searches over months of road data stored in your local index.

Yes. You can use the allowed_tools filter to restrict which Google Roads functions the agent can access. This keeps your index clean and prevents the agent from calling expensive tools unnecessarily.

No, because LlamaIndex grounds the agent's responses in the raw API data stored in the index. The agent cites the specific road segments and limits retrieved from the MCP Server.

Simply trigger the MCP tool again to fetch fresh coordinates or limits. LlamaIndex updates the relevant nodes in your vector store to reflect the latest fleet status.

Your API calls and the resulting data stay within your local environment. LlamaIndex stores this info in your private vector database, ensuring no third party accesses your tracking history.

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