How to Use the Haversine Distance Engine MCP in LlamaIndex

Index geographic distance data into your LlamaIndex knowledge base with the Haversine Distance Engine.

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Connect Haversine Distance Engine MCP to LlamaIndex

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

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Semantic indexing of distance results

LlamaIndex stores the output of `calculate_distance` as part of your vector index. You can search over past location calculations easily. This makes your RAG system aware of actual geographic proximity between points. It turns raw math into queryable knowledge.

Ground your RAG with precise math

Your agent calls `calculate_distance` to get real-time data for your documents. This ensures your knowledge base stays current with live coordinates. It removes the risk of hallucinated distances in your answers. You provide grounded facts based on verified spherical math.

Automated MCP tool indexing

The `calculate_distance` tool integrates with your existing workflow via the LlamaIndex MCP spec. You call it like any other local function. It handles the conversion between geographic points and structured data automatically. You focus on the retrieval strategy instead of the plumbing.

Setup guide

Set up Haversine Distance Engine MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Haversine Distance Engine MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Haversine Distance Engine tools.",
)
response = await agent.run("List recent Haversine Distance Engine data")

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Common questions about Haversine Distance Engine MCP in LlamaIndex

Yes. You can include this server in your tool list to ground your agent's responses in accurate distance data.

You can calculate distances and use the output as metadata for your nodes. This allows for proximity-based filtering during retrieval.

You can inspect the tool outputs directly in your index. Every distance result is stored as a clear, numeric value.

The server supports asynchronous operations natively. You can await the tool calls within your async agent loops without blocking.

The server operates in an ephemeral sandbox. It treats your GPS coordinates as transient inputs and destroys them after the calculation completes.

Use it with your favorite AI tools

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

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