How to Use the IPGeolocation (IP Intelligence & Time) MCP in Pydantic AI

Validate IP location and timezone payloads at runtime using Pydantic AI to ensure type-safe agent decisions.

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Connect IPGeolocation (IP Intelligence & Time) MCP to Pydantic AI

Create your Vinkius account to connect IPGeolocation (IP Intelligence & Time) 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 IPGeolocation (IP Intelligence & Time) with Pydantic AI

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Enforce schema validation with this Pydantic AI tool

The `get_location` tool resolves IP addresses into precise coordinates and country codes, returning structured JSON that Pydantic AI validates instantly. This MCP server integration prevents your agent from acting on corrupt or incomplete location payloads. If the geolocation API returns an unexpected data type, your Pydantic AI application raises a validation error immediately. This loud failure prevents downstream code from executing with bad geographic assumptions.

Guarantee type-safe timezone lookups

The `get_timezone` tool provides the exact local time and offset details for any given IP address. Pydantic AI parses this response against strict Python type hints, ensuring your agent always gets clean integer offsets and valid timezone strings. You don't have to worry about silent failures where a Pydantic AI agent guesses a timezone format. The framework guarantees that the model only receives data that conforms exactly to your defined schema.

Parse local solar cycles with zero runtime surprises

The `get_astronomy` tool retrieves sunrise, sunset, and moon phase data for any resolved IP address. This structured environmental data is parsed directly into Pydantic models before your Pydantic AI agent can use it to make scheduling decisions. The validation layer sits between the MCP server and your Pydantic AI agent, ensuring consistent data structures everywhere. This setup works across any LLM backend you choose, from local models to commercial APIs.

Setup guide

Set up IPGeolocation (IP Intelligence & Time) 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": {
        "ipgeolocation-ip-intelligence-time-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to IPGeolocation (IP Intelligence & Time) tools.",
)

result = await agent.run("List recent IPGeolocation (IP Intelligence & Time) transactions")
print(result.output)

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Common questions about IPGeolocation (IP Intelligence & Time) MCP in Pydantic AI

Use the MCPToolset class initialized with your server's HTTP endpoint. Pass this toolset directly into the toolsets list when instantiating your Pydantic AI Agent.

Yes. The unified toolset configuration supports both Streamable HTTP and SSE transports, allowing you to connect to your externally running MCP server securely.

The framework will raise a validation error at runtime. This prevents your agent from hallucinating missing data or operating with null values where strings are expected.

Yes. Because this framework is model-agnostic, your validated tools will work just as well with local models as they do with hosted commercial APIs.

The server only processes the target IP address to determine its location. Your data is protected by Vinkius's zero-trust infrastructure, which runs the process in an isolated sandbox and never stores the IP logs.

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