TomTom MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect TomTom through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to TomTom "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in TomTom?"
)
print(result.data)
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.
Pydantic AI validates every TomTom tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the TomTom MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the TomTom MCP Server
Pydantic AI provides unique advantages when paired with TomTom through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your TomTom integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your TomTom connection logic from agent behavior for testable, maintainable code
TomTom + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the TomTom MCP Server delivers measurable value.
Type-safe data pipelines: query TomTom with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple TomTom tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query TomTom and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock TomTom responses and write comprehensive agent tests
TomTom MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect TomTom to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting TomTom to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiTomTom + Pydantic AI FAQ
Common questions about integrating TomTom MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.