How to Use the TomTom Alternative MCP in LlamaIndex

Q: How does TomTom Alternative help index POIs in LlamaIndex?

You run fuzzysearch to find the relevant addresses and POIs. The output is then treated as structured data, which you can explicitly include when building your vector store's knowledge base.

Q: Can I query past routing results with TomTom Alternative in LlamaIndex?

Yes. By indexing the outputs of calculateroute and calculateadvancedroute, you create a history. Your agent can then answer questions grounded in that API data, avoiding hallucinations.

Q: What is the best way to handle geocoding results with TomTom Alternative for LlamaIndex?

After running geocode, save those coordinate pairs into your index. You can then combine them with document metadata (like timestamps or source documents) for highly specific queries.

Q: What kind of data does TomTom Alternative touch regarding traffic incidents?

The gettrafficincidents tool provides real-time traffic incident and flow information. You can index these reports to track changes over time, letting users ask questions like: 'Was the I-5 clear last Tuesday?'

Q: How do I ensure data privacy when using TomTom Alternative with LlamaIndex?

This server primarily touches geographical coordinates and address strings. Since you are indexing this API data into a private vector store, control over the access to that index is key.

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Connect TomTom Alternative MCP to LlamaIndex

Create your Vinkius account to connect TomTom Alternative 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 Alternative with LlamaIndex

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Search for Addresses Using MCP Server

Start by running `fuzzy_search`. You feed it a single line of text—it could be an address or a POI name. The output is highly relevant matches that you can then index into your knowledge base. It's much better than a simple keyword search. The results aren't just printed; they become part of the data set you build, making them searchable for future queries.

Calculate Optimal Routes in LlamaIndex

Want to know the best path from point A to point B? You call `calculate_route`. The tool returns coordinates and directions. By passing this output into your RAG pipeline, you index not just the data, but the *reasoning* behind the route. This means later queries can retrieve answers like: 'What was the fastest route calculated last week for client X?'

Access Global Mapping Data via MCP Server

The server exposes `geocode`, which converts addresses into coordinates. You'll want to index these coordinate pairs alongside related documents. This allows your RAG application to answer questions like, 'Which document relates to the coordinates of this address?' It combines structured API data with unstructured text data for powerful retrieval.

Setup guide

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

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Common questions about TomTom Alternative MCP in LlamaIndex

You run `fuzzy_search` to find the relevant addresses and POIs. The output is then treated as structured data, which you can explicitly include when building your vector store's knowledge base.

Yes. By indexing the outputs of `calculate_route` and `calculate_advanced_route`, you create a history. Your agent can then answer questions grounded in that API data, avoiding hallucinations.

After running `geocode`, save those coordinate pairs into your index. You can then combine them with document metadata (like timestamps or source documents) for highly specific queries.

The `get_traffic_incidents` tool provides **real-time traffic incident and flow information**. You can index these reports to track changes over time, letting users ask questions like: 'Was the I-5 clear last Tuesday?'

This server primarily touches **geographical coordinates and address strings**. Since you are indexing this API data into a private vector store, control over the access to that index is key.

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