How to Use the Unanet MCP in CrewAI

Q: What specific data types are exposed by this MCP Server for privacy considerations?

The server provides user profile details (users()), project metadata, time sheets, and expense reports. Because multiple agents touch these records, strict access control is vital.

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Connect Unanet MCP to CrewAI

Create your Vinkius account to connect Unanet 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 Unanet with CrewAI

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Conducting Project Status Audits with MCP Server

A specialized 'Project Manager Agent' uses `projects()` and `users()`. This agent first gathers a list of all active projects, then cross-references those against the personnel listed in Unanet. The crew collaborates to build a complete roster showing who is assigned to what project. It’s designed for monitoring and identifying gaps automatically.

Financial Compliance Checks using Unanet Data

You can create an 'Audit Agent' that focuses on financial compliance. This agent uses `expenses()` and `timesheets()`, checking the records for a specified user or time range. The shared memory allows one agent to gather raw data, and another specialized agent to analyze it for discrepancies—all without human intervention.

Personnel Management Tasks with MCP Server

A dedicated 'HR Agent' uses the `users()` tool. It pulls all employee records from Unanet so the crew can perform bulk operations, like updating roles or generating reports. The multi-agent setup means one agent researches the user list, and another takes action based on that research, providing a clear audit trail.

Setup guide

Set up Unanet 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 Unanet tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="Unanet Analyst",
        goal="Access and analyze Unanet data via MCP.",
        backstory="Expert analyst with direct Unanet access.",
        tools=mcp_tools,
    )

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

CrewAI uses role-based specialization. You define agents—say, an 'Analyst' and an 'Executor'—and give them access to the MCP Server tools like `projects()`. They then pass results between each other.

Yes. You can assign a 'Monitor Agent' that watches for specific triggers, maybe checking if timesheet data is missing using `timesheets()`. If it detects an issue, a separate 'Escalation Agent' takes over.

You can build complex operations. For example, the crew could combine `projects()`, `users()`, and `expenses()` to automatically generate a full departmental spending report for an entire project.

It does. The shared memory mechanism lets agents pool information gathered from various tools, like running `users()` and then immediately feeding that list into the `projects()` tool for filtering.

The server provides user profile details (`users()`), project metadata, time sheets, and expense reports. Because multiple agents touch these records, strict access control is vital.

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