How to Use the Haversine Distance Engine MCP in Pydantic AI

Ensure type-safe distance calculations in your Pydantic AI pipelines with zero coordinate schema drift.

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Connect Haversine Distance Engine MCP to Pydantic AI

Create your Vinkius account to connect Haversine Distance Engine to Pydantic AI 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 Pydantic AI

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Strict type validation for geographic coordinates

Pydantic AI guarantees that every input and output matches your schemas at runtime. When your agent calls `calculate_distance`, the framework validates the latitude and longitude float values before the tool executes. This prevents malformed data from breaking your application. If the model tries to pass an invalid string instead of a float, the system throws a clear validation error.

Model-agnostic spatial math with this MCP Server

You are not locked into one model provider. This MCP Server works whether you run your Pydantic AI agent on Anthropic, OpenAI, or local open-source models. The tool schema remains consistent across all backends. Your agent gets the same precise distance outputs regardless of which LLM is driving the logic.

Fail-loud execution prevents silent failures

Silent data corruption can ruin a logistics pipeline. If this engine returns unexpected data, Pydantic AI catches it instantly and halts execution. This strictness is critical for production routing over MCP. You can trust that the miles and kilometers returned by `calculate_distance` are mathematically sound and correctly typed.

Setup guide

Set up Haversine Distance Engine MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "haversine-distance-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Haversine Distance Engine tools.",
)

result = await agent.run("List recent Haversine Distance Engine transactions")
print(result.output)

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Common questions about Haversine Distance Engine MCP in Pydantic AI

Use the unified toolset class with your server's HTTP address. Pass it directly to your agent's toolset list to expose the distance calculation functions in your MCP workspace.

Yes, the server validates that latitude falls between -90 and 90, and longitude between -180 and 180. Pydantic AI then enforces these types on the client side.

Absolutely. Since the framework is model-agnostic, you can connect the toolset to local runners like Ollama and get the exact same mathematical precision.

It supports both Streamable HTTP and SSE transports. This flexibility allows you to connect to externally running servers without complex network configurations.

No coordinate data is logged or stored. The server runs in a zero-trust, ephemeral V8 sandbox, ensuring your GPS coordinates are used only for the active calculation.

Use it with your favorite AI tools

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

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