How to Use the Mapulus MCP in CrewAI

Q: How do I set up a CrewAI agent to use the Mapulus MCP Server?

You pass the server URL into the mcps parameter of your Agent constructor. The crew will automatically discover and use the available tools.

Q: Does Mapulus allow for selective tool exposure in CrewAI?

You can use toolfilter in your configuration to limit which tools an agent can call. This is helpful if you want to restrict an agent to only read-only data.

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Connect Mapulus MCP to CrewAI

Create your Vinkius account to connect Mapulus to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Mapulus with CrewAI

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Perplexity

Autonomous crews using Mapulus

Assign a researcher agent to use `list_data_topics` and `search_boundaries` to gather intelligence. The crew shares this memory so the analyst agent knows exactly where to look. It removes the need for manual oversight. Your agents work through the data sequentially or hierarchically based on your setup.

Role-based spatial analysis

Create specialized agents for different tasks. A monitor agent can check for updates via `search_suburbs`, while an executive agent makes decisions based on the results. This specialization ensures your spatial analytics remain accurate. Each agent focuses only on the tools it needs to complete its specific mission.

Direct MCP integration

Connect your agents to the server by passing the endpoint directly to the agent's MCP list. It's a quick way to give your crew access to the full suite of tools. Your agents can invoke `get_demographics` whenever they need to validate a location. They handle the interaction autonomously.

Setup guide

Set up Mapulus 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 Mapulus tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Mapulus 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="Mapulus Analyst",
        goal="Access and analyze Mapulus data via MCP.",
        backstory="Expert analyst with direct Mapulus access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Mapulus 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 Mapulus. 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 Mapulus MCP in CrewAI

You pass the server URL into the `mcps` parameter of your Agent constructor. The crew will automatically discover and use the available tools.

Yes, you can define a crew where each agent handles a different location. They use the server to pull data for their assigned area and report back to a lead agent.

You can use `tool_filter` in your configuration to limit which tools an agent can call. This is helpful if you want to restrict an agent to only read-only data.

CrewAI uses shared memory for agents in the same crew. Once one agent retrieves data via `get_postcode_data`, the entire team can access that information for their tasks.

We maintain a zero-trust policy. Every request for boundary or demographic records is isolated, meaning no data is cached or stored after the agent's task is finished.

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