How to Use the Treinta App MCP in CrewAI

Q: What data does Treinta App touch regarding privacy?

This server touches client debt profiles. The crew can use getclient to fetch this financial information, and a specialized agent can then generate a summary report based on the returned debt details.

Q: Does Treinta App support listing all book flow transactions with CrewAI?

Yes. You assign an agent to use listtransactions. The shared memory ensures that every transaction record is available for subsequent agents to analyze, creating a complete audit trail.

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Connect Treinta App MCP to CrewAI

Create your Vinkius account to connect Treinta App 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 Treinta App with CrewAI

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Conduct Full Client Audits

You can assign an agent to run `list_clients` and another agent to use `get_client`. The system then aggregates all results, allowing specialized agents to build a comprehensive debt report from the combined data. This role-based specialization means one agent gathers the list, while another analyzes the financial profile of each individual client.

Analyze Full Business Performance

Assigning roles lets your crew run `get_business_stats`. One agent pulls the raw data via `get_business_stats`, and a second, specialized analysis agent interprets that data to provide actionable insights for management. This is ideal for autonomous monitoring. The setup allows a monitor agent to watch these macro P&L operations dashboards continuously.

Manage Inventory Life Cycles

The crew can autonomously manage inventory by first running `list_products` (Agent 1: Research), then calculating necessary changes, and finally executing `update_product_stock` (Agent 2: Action). This hierarchical execution means the process moves from observation to action automatically without needing manual handoffs between steps.

Setup guide

Set up Treinta App 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 Treinta App tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You assign an agent the role of 'Client Lister' and give it access to `list_clients`. The crew then runs this tool, gathering all registered customer data into shared memory for subsequent analysis.

Yes. You can set up a sequence where one agent fetches sales using `list_sales`, and another agent summarizes the trends found in the data, providing an automated report.

You can create a transaction workflow. An 'Action Agent' uses `create_transaction`, while a 'Review Agent' waits for the outcome, ensuring that the entire record flow is monitored automatically.

This server touches client debt profiles. The crew can use `get_client` to fetch this financial information, and a specialized agent can then generate a summary report based on the returned debt details.

Yes. You assign an agent to use `list_transactions`. The shared memory ensures that every transaction record is available for subsequent agents to analyze, creating a complete audit trail.

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