How to Use the Aracaju MCP in CrewAI

Q: How do I connect the Aracaju MCP Server to CrewAI?

Install the framework using pip install crewai 'crewaitools'. You pass the Vinkius URL directly into the mcps array in your agent definition.

Q: How do I restrict certain CrewAI agents from accessing Aracaju contracts?

Import MCPServerHTTP from crewai.mcp and apply a toolfilter. This explicitly defines which agent gets access to which municipal database.

Deploy autonomous agent teams to audit Aracaju's municipal data using CrewAI.

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Connect Aracaju MCP to CrewAI

Create your Vinkius account to connect Aracaju 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 Aracaju with CrewAI

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Autonomous audits via MCP Server.

Your agents use the `list_expenses` and `list_revenues` tools to conduct financial audits of the municipality. CrewAI allows you to assign these tasks to a specialized financial analyst agent. This specific agent pulls the raw numbers and shares them in memory so a secondary reporting agent can draft the final analysis.

Track procurement with specialized roles.

The `list_bids` and `list_contracts` tools feed active tender information directly into your agent crew. You set up a monitor agent to watch for new public bids. When a relevant contract appears, a moderator agent takes over to evaluate the terms and escalate the opportunity to your sales team.

Filter payroll data automatically.

Running the `list_personnel` tool extracts public servant payroll details for the city. Advanced setups use MCPServerHTTP with a tool_filter to expose only this specific function to a designated HR analysis agent. This prevents other agents in the crew from accidentally triggering unrelated queries.

Setup guide

Set up Aracaju 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 Aracaju tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Install the framework using pip install crewai 'crewai[tools]'. You pass the Vinkius URL directly into the mcps array in your agent definition.

They don't step on each other's toes. You assign specific municipal lookup tasks to different agents, and they share the retrieved transparency records through their shared memory pool.

The framework handles stdio, SSE, and Streamable HTTP. You configure this based on how you structure your crew's execution environment.

Import MCPServerHTTP from crewai.mcp and apply a tool_filter. This explicitly defines which agent gets access to which municipal database.

The server interacts solely with public transparency data like municipal bids, expenses, and payroll. Vinkius processes your requests in a zero-trust, ephemeral sandbox that destroys itself immediately after your crew finishes its task.

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