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 LlamaIndex 10 tools — connect in under 2 minutes

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Radar as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Radar. "
            "You have 10 tools available."
        ),
    )

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

asyncio.run(main())

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* 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.

LlamaIndex agents combine Radar tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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

Why Use LlamaIndex with the Radar MCP Server

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

Data-first architecture: LlamaIndex agents combine Radar tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Radar tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Radar, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Radar tools were called, what data was returned, and how it influenced the final answer

Radar + LlamaIndex Use Cases

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

Hybrid search: combine Radar real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Radar to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Radar for fresh data

Analytical workflows: chain Radar queries with LlamaIndex's data connectors to build multi-source analytical reports

Radar MCP Tools for LlamaIndex (10)

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

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

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Radar + LlamaIndex FAQ

Common questions about integrating Radar MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.