How to Use the TomTom Parking Availability MCP in LlamaIndex

Ground LlamaIndex answers in real-time TomTom Parking Availability data.

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Connect TomTom Parking Availability MCP to LlamaIndex

Create your Vinkius account to connect TomTom Parking Availability 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 Parking Availability with LlamaIndex

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Indexing search results with the MCP Server

When you run `search_parking_spots`, the output isn't just a temporary result—it becomes knowledge. LlamaIndex indexes this location and availability data into your vector store. This means past searches are searchable. You can query old session results to recall specific parking spots without having to re-run the search.

Using TomTom Parking Availability details in LlamaIndex

The `get_parking_details` tool output, which contains rich metadata, gets stored alongside your documents. The system treats this API data as factual knowledge. This capability lets you build RAG applications where the AI combines document context with live parking availability facts.

Checking service health via LlamaIndex

You can feed `check_api_status` results directly into your index. This creates a searchable log of API performance and operational status. Future queries about the TomTom Parking Availability system's reliability are answered by retrieving these logged, verifiable facts.

Setup guide

Set up TomTom Parking Availability 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 Parking Availability 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 Parking Availability tools.",
)
response = await agent.run("List recent TomTom Parking Availability 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 TomTom Parking. 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 TomTom Parking Availability MCP in LlamaIndex

LlamaIndex indexes API outputs into a searchable knowledge base. You combine live parking availability metadata with your documents, allowing the AI to answer questions grounded in actual search results.

The server touches location and availability metadata. These structured API outputs are converted into vector embeddings, making them semantically searchable within your index.

Absolutely. Because the tool output is indexed, you can run a query months later and get answers grounded in specific parking spot data from previous sessions.

The setup supports filtering tools and including multiple resources. This means you can build a unified index drawing knowledge from several different MCP sources.

This server touches location data and availability metadata. By indexing it, you must manage access control to ensure sensitive location coordinates aren't exposed through search results.

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