Where do I find my Radar API key?

Log into your Radar dashboard. Under 'Project Settings' or the dedicated API keys section, look for your 'Publishable Key'. This is the standard read-only key used for client integrations and MCP operations.

Should I use the Publishable Key or the Secret Key?

Always use the **Publishable Key** for this MCP server instance if possible, as the operations (geocoding, routing, lookup) are typically safe read-only spatial actions.

Can it calculate multi-stop routes like a delivery app?

Yes, but indirectly through distance comparisons. The basic routing API fetches times from Point A to Point B. The AI can chain these requests together to establish an estimated schedule contextually in your chat.

Radar MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Radar through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Radar "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Radar?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Radar MCP Server

Integrate Radar with an AI agent to bring enterprise-level location intelligence directly to your workflow. This server allows the AI to perform complex spatial lookups and geographical computations on your behalf.

Pydantic AI validates every Radar tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Geocoding & Reverse Geocoding — Convert readable addresses into exact coordinates (latitude/longitude), or vice versa.
Route Calculation — Determine distance and driving times between multiple locations, predicting transit metrics efficiently.
Geofencing & Context — Check whether specific coordinates fall within defined geographical boundaries (e.g., regions, stores, administrative borders).
IP Geolocation — Locate a user or device strictly based on an IP address.

The Radar MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Radar to Pydantic AI via MCP

Follow these steps to integrate the Radar MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Radar with type-safe schemas

Why Use Pydantic AI with the Radar MCP Server

Pydantic AI provides unique advantages when paired with Radar through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Radar integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Radar connection logic from agent behavior for testable, maintainable code

Radar + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Radar MCP Server delivers measurable value.

Type-safe data pipelines: query Radar with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Radar tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Radar and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Radar responses and write comprehensive agent tests

Radar MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Radar to Pydantic AI via MCP:

autocomplete

Provides address or place suggestions as a user types

calculate_route_distance

Calculates travel distance and duration between two points

calculate_routing_matrix

Calculates travel times and distances between multiple origins and destinations

forward_geocode

Converts a human-readable address into geographic coordinates (latitude and longitude)

get_location_context

Retrieves contextual information for a location, such as geofences and weather

ip_geocode

Retrieves geographic location information based on an IP address

reverse_geocode

Converts geographic coordinates into a human-readable address

search_geofences

Searches for active geofences near a specific location

search_places

Searches for nearby places (POIs) based on coordinates

validate_address

Validates and cleans up a structured address

Example Prompts for Radar in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Radar immediately.

"Geocode '1600 Amphitheatre Parkway, Mountain View, CA'."

"Find the driving distance between my office in San Francisco (lat, lng) and the San Jose airport."

"Locate the country based on the IP address 8.8.8.8."

Troubleshooting Radar MCP Server with Pydantic AI

Common issues when connecting Radar to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Radar + Pydantic AI FAQ

Common questions about integrating Radar MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Radar MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.