How to Use the Trengo MCP in CrewAI

Q: What data can I retrieve about contacts using CrewAI with Trengo?

The listcontacts tool gives your agents access to all necessary contact records. They can then use this list, combined with ticket data, to build comprehensive customer profiles.

Q: Can I check message history using CrewAI and Trengo?

Yes. By calling listmessages, your agents get the full conversation history. This is crucial for autonomous operations that need context before responding or escalating.

Q: What about webhooks when working with Trengo using CrewAI?

You can use createwebhook to establish new triggers. The crew structure allows one agent to monitor the webhook list, ensuring that no critical event is missed by the system.

Run multi-agent Trengo operations autonomously with CrewAI.

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Connect Trengo MCP to CrewAI

Create your Vinkius account to connect Trengo 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 Trengo with CrewAI

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Automate customer outreach and messaging.

A specialized agent can run `send_message` to a contact. You define the message body, and the agent ensures it gets delivered through Trengo's communication channels. This is great for autonomous follow-ups: one agent researches the client, another sends the message.

Research and list all support tickets.

One crew member can execute `list_tickets` to get a full view of support issues. Another specialized agent then reads that list and flags high-priority items for review. It's role-based specialization in action, turning raw data into actionable insights.

Identify all available communication channels.

Use `list_channels` to let your agents know every pathway Trengo supports. This information is shared memory across the entire crew. A third agent can then use this list to determine the optimal channel for a given customer interaction.

Setup guide

Set up Trengo 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 Trengo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

A dedicated 'Moderator Agent' watches the session. When a decision is reached, it executes `update_ticket`. This keeps the overall operation autonomous and traceable.

The `list_contacts` tool gives your agents access to all necessary contact records. They can then use this list, combined with ticket data, to build comprehensive customer profiles.

Yes. By calling `list_messages`, your agents get the full conversation history. This is crucial for autonomous operations that need context before responding or escalating.

You can use `create_webhook` to establish new triggers. The crew structure allows one agent to monitor the webhook list, ensuring that no critical event is missed by the system.

This server touches communication channels and messages. Agents will be handling live customer conversations across various platforms, so access controls are vital.

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