How to Use the Radar MCP in CrewAI

Q: Can CrewAI agents share Radar coordinate data between tasks?

Yes, CrewAI's shared memory allows a research agent using reversegeocode to pass the resolved physical address directly to a logistics agent in the next task.

Deploy specialized agent teams that collaborate on logistics and geofencing using CrewAI and Radar.

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Connect Radar MCP to CrewAI

Create your Vinkius account to connect Radar to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Radar with CrewAI

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Multi-Agent Logistics Coordination with CrewAI

CrewAI lets you set up a team of specialized agents to handle complex dispatch operations. A Router Agent can use `calculate_routing_matrix` to find the most efficient paths, while a Dispatcher Agent monitors active zones using `search_geofences`. Because these agents share memory, the coordinates resolved by the Router Agent via `forward_geocode` are instantly available to the rest of the crew. This eliminates redundant API calls and keeps your entire autonomous team aligned on the same coordinates.

Automated Address Verification Crews

Create an autonomous pipeline where a Validator Agent runs raw customer inputs through `validate_address`. If the address is messy, a Corrector Agent uses `autocomplete` to suggest the correct postal format before passing it to the delivery crew. Once the address is clean, a third agent can run `get_location_context` to check for local delivery restrictions. The entire process runs sequentially without requiring any manual developer intervention.

IP-Based Security and Place Audits

Deploy a security agent that uses `ip_geocode` to verify where user requests are originating. If the coordinates look suspicious, the agent triggers a secondary lookup using `search_places` to verify if the location matches a known commercial address. By exposing this MCP Server to your CrewAI team, you can build autonomous fraud prevention loops. The agents analyze the geographic context and flag anomalies before passing the clean data to your main database.

Setup guide

Set up Radar MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Radar tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Radar Analyst",
    goal="Access and analyze Radar data via MCP.",
    backstory="Expert analyst with direct Radar access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Radar transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Radar Analyst",
        goal="Access and analyze Radar data via MCP.",
        backstory="Expert analyst with direct Radar access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Radar transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Radar. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Radar MCP in CrewAI

Pass the Vinkius MCP Server URL directly in the agent's configuration array. You can limit access so only your Router Agent can call `calculate_route_distance`.

Yes, CrewAI's shared memory allows a research agent using `reverse_geocode` to pass the resolved physical address directly to a logistics agent in the next task.

Define a manager agent in CrewAI to oversee the workflow. The manager agent will delegate `calculate_routing_matrix` to a sub-agent and verify the results before finalizing the route.

Yes, CrewAI's HTTP client supports both standard SSE and Streamable HTTP transports. This ensures stable connections when agents are performing heavy lookups like `search_places`.

Yes, Vinkius runs this MCP Server inside zero-trust, ephemeral sandboxes. The data is encrypted in transit and wiped completely from the execution environment as soon as your CrewAI agents finish their tasks.

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