How to Use the Haversine Distance Engine MCP in LangChain

Calculate precise geographic distances in your LangChain agents using the Haversine Distance Engine.

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Connect Haversine Distance Engine MCP to LangChain

Create your Vinkius account to connect Haversine Distance Engine to LangChain 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 LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain distance calculations in LangChain

Your LangChain agent uses `calculate_distance` to compute exact paths between points. It treats these coordinates as inputs for downstream reasoning tasks. This MCP Server provides the raw numbers your chain needs to make routing decisions. You get reliable math without writing custom geometry code.

Streamline agent decision logic

Feed the output of `calculate_distance` directly into your next agent step. You define the flow, and the agent handles the math behind the scenes. This keeps your agent loop clean and focused on navigation logic. It connects geographic data to your existing chains with minimal overhead.

Traceable distance operations

Every call to the `calculate_distance` tool appears in your LangSmith traces. You see exactly what coordinates the agent processed. Debugging your multi-step pipelines becomes a simple matter of checking the logs. You catch rounding errors or bad input data before they reach your final output.

Setup guide

Set up Haversine Distance Engine MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Haversine Distance Engine tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "haversine-distance-engine-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Haversine Distance Engine transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Haversine Distance Engine MCP in LangChain

You install the adapters and pass the tool to your agent constructor. The LangChain agent then automatically detects the `calculate_distance` function.

Yes. The tool returns the distance as a standard numeric value that you can inject directly into your prompt templates.

Absolutely. You can aggregate this server alongside others in your client configuration to build complex, multi-tool workflows.

The server validates your inputs immediately. If the latitude or longitude values are out of bounds, it throws an error before calculating.

Your coordinate data stays within the execution session. The engine processes your inputs in memory and does not persist them after the request finishes.

Use it with your favorite AI tools

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

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