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 LangChain 10 tools — connect in under 2 minutes

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Radar through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "radar": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Radar, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

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.

LangChain's ecosystem of 500+ components combines seamlessly with Radar through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Radar MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Radar via MCP

Why Use LangChain with the Radar MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Radar MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Radar queries for multi-turn workflows

Radar + LangChain Use Cases

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

RAG with live data: combine Radar tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Radar, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Radar tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Radar tool call, measure latency, and optimize your agent's performance

Radar MCP Tools for LangChain (10)

These 10 tools become available when you connect Radar to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Radar + LangChain FAQ

Common questions about integrating Radar MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.