How to Use the TomTom MCP in LlamaIndex

Q: How can I query traffic incidents using TomTom and LlamaIndex?

First, run gettrafficincidents to gather real-time data within a bounding box. By indexing this output, you build historical context that lets your agent answer questions about past congestion patterns.

Q: Can LlamaIndex use TomTom for fuzzy geocoding?

Yes. When fuzzygeocoding converts an address string to coordinates, the resulting coordinate pair can be indexed. This means you can search your knowledge base for addresses that match a certain general area.

Q: What if I need TomTom's route data using LlamaIndex?

Run calculateroute to get the polyline and summary. Passing this output to LlamaIndex turns it into queryable facts, so your agent can reference a specific historical trip detail later.

Q: Does TomTom help with structured geocoding using LlamaIndex?

If you use structuredgeocoding to get precise coordinates based on explicit parameters, LlamaIndex indexes those successful coordinate results. This strengthens your knowledge base with validated location data.

Build knowledge graphs with LlamaIndex using TomTom’s global mapping and routing tools.

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Connect TomTom MCP to LlamaIndex

Create your Vinkius account to connect TomTom 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 TomTom with LlamaIndex

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Indexing Route Calculations for LlamaIndex

When your agent runs `calculate_route`, it returns a route polyline and a summary. Instead of just passing that data through, LlamaIndex indexes the entire output into your vector store. Later, you can query this knowledge base to ask questions like, 'What was the typical travel time from Point A to Point B?' and get an answer grounded in past API results.

Searchable POI Metadata with LlamaIndex

You don't just need a list of gas stations. Running `get_poi_details` retrieves rich metadata for a specific Point of Interest ID. LlamaIndex takes this detailed text and indexes it. This lets you query your knowledge base semantically, asking things like, 'What are the accepted payment methods at POIs near my old work address?'

Geo-Spatial Querying with MCP Server

Want to know what area is reachable in 45 minutes? You run `calculate_reachable_range` with coordinates and a time budget. The resulting description of the accessible zone becomes indexed data. This allows you to build RAG applications that answer complex, location-based queries by searching through historical API output.

Setup guide

Set up TomTom 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 TomTom 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 TomTom tools.",
)
response = await agent.run("List recent TomTom data")

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Common questions about TomTom MCP in LlamaIndex

First, run `get_traffic_incidents` to gather real-time data within a bounding box. By indexing this output, you build historical context that lets your agent answer questions about past congestion patterns.

Yes. When `fuzzy_geocoding` converts an address string to coordinates, the resulting coordinate pair can be indexed. This means you can search your knowledge base for addresses that match a certain general area.

Run `calculate_route` to get the polyline and summary. Passing this output to LlamaIndex turns it into queryable facts, so your agent can reference a specific historical trip detail later.

If you use `structured_geocoding` to get precise coordinates based on explicit parameters, LlamaIndex indexes those successful coordinate results. This strengthens your knowledge base with validated location data.

This MCP Server touches geographical and descriptive text data (POI metadata). The system manages all input tokens via a single endpoint token, ensuring your access remains within Vinkius's security framework.

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