How to Use the Haversine Distance Engine MCP in CrewAI

Q: How do I configure the Haversine Distance Engine for CrewAI?

Install crewaitools. Add the server URL to the mcps list when defining your agent. The framework automatically discovers the tools.

Give your CrewAI agents the ability to calculate exact geographic distances.

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Connect Haversine Distance Engine MCP to CrewAI

Create your Vinkius account to connect Haversine Distance Engine 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.

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Setup Haversine Distance Engine with CrewAI

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Equip Your Logistics Crew

The `calculate_distance` tool gives your CrewAI agents a reliable way to measure space. A researcher agent finds the coordinates, then passes them to a logistics agent. That specific persona uses this tool to figure out the exact mileage. Building specialized teams works better than massive monolithic prompts. The MCP standard makes this math happen fast and accurately. Your agents share this context in memory and collaborate to build complex routing plans.

Autonomous Geospatial Analysis

Executing `calculate_distance` allows a moderator agent to verify travel times automatically. When one agent proposes a delivery route, the moderator can double-check the distance between stops. Everything happens sequentially or hierarchically. Setup is dead simple. Pass the endpoint URL directly into the `mcps` array on your agent definition. The CrewAI framework handles the tool binding behind the scenes.

Filter MCP Server Tools for Specific Agents

Assigning `calculate_distance` to a specific persona keeps your crew focused. Developers use `MCPServerHTTP` from `crewai.mcp` and apply a tool filter. This code ensures only the routing agent has access to the geospatial math. This MCP Server acts as a dedicated calculator for your autonomous operations. It eliminates the need for agents to write their own faulty Python distance scripts. They just call the tool and get the facts.

Setup guide

Set up Haversine Distance Engine 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 Haversine Distance Engine tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Haversine Distance Engine 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="Haversine Distance Engine Analyst",
        goal="Access and analyze Haversine Distance Engine data via MCP.",
        backstory="Expert analyst with direct Haversine Distance Engine access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Haversine Distance Engine 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 Haversine Engine. 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 Haversine Distance Engine MCP in CrewAI

Install `crewai[tools]`. Add the server URL to the `mcps` list when defining your agent. The framework automatically discovers the tools.

Yes. Any agent with the server attached can call the tool. They can also share the calculated distances through CrewAI's shared memory.

It runs synchronously but incredibly fast. The agent waits a fraction of a second for the math to finish, then continues its task.

It provides both kilometers and miles simultaneously. Your agent decides which metric fits the current analysis.

The server runs in a zero-trust environment. The exact latitude and longitude points are processed in memory and immediately discarded. Nobody can see where your agents are routing.

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