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 CrewAI 10 tools — connect in under 2 minutes

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Radar through Vinkius, pass the Edge URL in the `mcps` parameter and every Radar tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Radar Specialist",
    goal="Help users interact with Radar effectively",
    backstory=(
        "You are an expert at leveraging Radar tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Radar "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

When paired with CrewAI, Radar becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Radar tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Radar

Why Use CrewAI with the Radar MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Radar through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Radar + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Radar for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Radar, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Radar tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Radar against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Radar MCP Tools for CrewAI (10)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Radar + CrewAI FAQ

Common questions about integrating Radar MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.