How to Use the GraphHopper MCP in CrewAI

Deploy autonomous logistics crews using CrewAI and the GraphHopper MCP server for specialized, multi-agent fleet operations.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect GraphHopper MCP to CrewAI

Create your Vinkius account to connect GraphHopper to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup GraphHopper with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Specialized routing crews for CrewAI

Assign a dedicated agent to use `calculate_routing_matrix` while another agent focuses on fleet optimization. This separation of concerns allows your crew to handle complex logistics without overloading a single process. Your agents can share memory to build a complete picture of the delivery zone. One agent identifies the stops while the other finds the most efficient sequence to visit them.

Autonomous geocoding with CrewAI

Let your research agent use `search_geocode` to identify new delivery points and pass them to the operations agent. The crew handles the entire chain from finding the location to calculating the arrival time. This removes the need for manual input. Your crew can monitor incoming orders and automatically route them based on the current state of your fleet and road network.

Reachability analysis in CrewAI

Configure an agent to use `calculate_reachability_polygon` to define delivery zones for your drivers. The crew can then adjust these zones dynamically based on traffic and road conditions. This keeps your delivery windows realistic. By having an agent constantly re-evaluating the reachability, your team avoids over-promising on transit times to retail clients.

Setup guide

Set up GraphHopper MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke GraphHopper tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="GraphHopper Analyst",
    goal="Access and analyze GraphHopper data via MCP.",
    backstory="Expert analyst with direct GraphHopper access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent GraphHopper transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="GraphHopper Analyst",
        goal="Access and analyze GraphHopper data via MCP.",
        backstory="Expert analyst with direct GraphHopper access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent GraphHopper transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GraphHopper. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect GraphHopper now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about GraphHopper MCP in CrewAI

You assign specific routing tools to specific agents. This ensures that only the agents responsible for logistics can interact with the GraphHopper server, keeping your crew's tasks focused.

Yes. Once you define the roles and the goal, the crew executes the routing plan autonomously. You can set up monitoring agents to log the progress of every tool call.

The MCP server provides a shared interface for your agents. Since they run in the same crew session, they can reference previous outputs to make better decisions.

The server is built to handle high-concurrency requests. Your crew can perform multiple matrix calculations in parallel without hitting bottlenecks.

Your delivery coordinates are treated as transient data for the routing calculation. The system isolates this information, ensuring it is never persisted on our servers after the task is done.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript