TomTom MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add TomTom as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 TomTom. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in TomTom?"
)
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 TomTom MCP Server
Connect your TomTom API account directly to any AI agent to unlock enterprise-grade geospatial and logistical capabilities native to your platform. Convert complex addresses instantly, evaluate driving routes based on exact origin and destination coordinates, and visualize live traffic blocks directly through chat queries.
LlamaIndex agents combine TomTom tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Precision Geocoding — Process any physical address string into absolute geographic latitude/longitude coordinates using fuzzy logic or structured fields, as well as reversing coordinates back to plain street names
- Route Computation — Calculate the exact travel time, polyline geometry, and distance for a trip between two precise coordinates
- Real-Time Traffic — Map traffic incidents (accidents, constructions, jams) constrained within a bounding box, or survey the traffic flow speed of a particular avenue segment
- Poi Discovery — Find global Points of Interest based on categories (e.g., hospitals, fuel) and retrieve rich contact metadata or opening hours for specific locations
- Travel Boundaries — Calculate reachable ranges (polygonal limits) to understand exactly how far your fleet or agents can travel within a set time budget
The TomTom MCP Server exposes 10 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 TomTom to LlamaIndex via MCP
Follow these steps to integrate the TomTom 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 10 tools from TomTom
Why Use LlamaIndex with the TomTom MCP Server
LlamaIndex provides unique advantages when paired with TomTom through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine TomTom tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain TomTom tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query TomTom, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what TomTom tools were called, what data was returned, and how it influenced the final answer
TomTom + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the TomTom MCP Server delivers measurable value.
Hybrid search: combine TomTom real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query TomTom 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 TomTom for fresh data
Analytical workflows: chain TomTom queries with LlamaIndex's data connectors to build multi-source analytical reports
TomTom MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect TomTom to LlamaIndex via MCP:
autocomplete_place_search
Provide a partial string and optional bias coordinates. Provides predictive location suggestions based on partial input
calculate_reachable_range
Provide center coordinates and a time budget in seconds. Calculates an area reachable within a specific time or distance budget
calculate_route
Returns the route polyline and a summary. Calculates a route and travel time between two points
fuzzy_geocoding
Converts a physical address string into geographic coordinates using fuzzy matching
get_poi_details
Retrieves rich metadata for a specific point of interest ID
get_traffic_flow_segment
Provide center coordinates. Retrieves the traffic flow speed and quality for a specific road segment
get_traffic_incidents
Provide min/max lat/lon coordinates. Retrieves real-time traffic incident details within a bounding box
reverse_geocoding
Converts geographic coordinates into a physical address
search_poi_by_category
Provide a category name and a center coordinate. Searches for points of interest (POIs) near a location by category (e.g., gas stations, hospitals)
structured_geocoding
Provide parameters like countryCode and postalCode. Performs geocoding using explicit address components (e.g., street, city, zip)
Example Prompts for TomTom in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with TomTom immediately.
"Convert these coordinates into an address: Lat 40.7128, Lon -74.0060."
"Check for any traffic incidents on the 101 freeway bounded roughly by these dimensions."
Troubleshooting TomTom MCP Server with LlamaIndex
Common issues when connecting TomTom to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTomTom + LlamaIndex FAQ
Common questions about integrating TomTom 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 TomTom 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.
Connect TomTom to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.