TomTom MCP Server for CrewAI 10 tools — connect in under 2 minutes
Connect your CrewAI agents to TomTom through Vinkius, pass the Edge URL in the `mcps` parameter and every TomTom tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="TomTom Specialist",
goal="Help users interact with TomTom effectively",
backstory=(
"You are an expert at leveraging TomTom tools "
"for automation and data analysis."
),
# Your Vinkius token. get it at cloud.vinkius.com
mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)
task = Task(
description=(
"Explore all available tools in TomTom "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 10 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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.
When paired with CrewAI, TomTom becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call TomTom tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
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 CrewAI 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 CrewAI via MCP
Follow these steps to integrate the TomTom MCP Server with CrewAI.
Install CrewAI
Run pip install crewai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Customize the agent
Adjust the role, goal, and backstory to fit your use case
Run the crew
Run python crew.py. CrewAI auto-discovers 10 tools from TomTom
Why Use CrewAI with the TomTom MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with TomTom through the Model Context Protocol.
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
TomTom + CrewAI Use Cases
Practical scenarios where CrewAI combined with the TomTom MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries TomTom for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff
Scheduled intelligence reports: set up a crew that periodically queries TomTom, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain TomTom tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow
Compliance and audit automation: a compliance agent queries TomTom against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
TomTom MCP Tools for CrewAI (10)
These 10 tools become available when you connect TomTom to CrewAI 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 CrewAI
Ready-to-use prompts you can give your CrewAI 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 CrewAI
Common issues when connecting TomTom to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
TomTom + CrewAI FAQ
Common questions about integrating TomTom MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.Can different agents in the same crew use different MCP servers?
mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.What happens when an MCP tool call fails during a crew run?
Can CrewAI agents call multiple MCP tools in parallel?
process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.Can I run CrewAI crews on a schedule (cron)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect TomTom with your favorite client
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect TomTom to CrewAI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.