How to Use the Trengo MCP in AutoGen

Q: Can I list team members using Trengo and AutoGen?

Yes. One agent can call listteammembers to get the user roster, and another agent reviews that list to assign ownership to a newly created ticket via createticket, achieving consensus on assignment.

Build consensus-driven workflows for Trengo using AutoGen.

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Connect Trengo MCP to AutoGen

Create your Vinkius account to connect Trengo to AutoGen 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 AutoGen

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MCP Server Negotiation

AutoGen lets multiple agents discuss the best path forward. For example, one agent might call `list_tickets` to identify a problem. A second agent reviews the ticket details via `get_ticket`. They debate what action is needed and decide if calling `update_ticket` or `send_message` is the optimal consensus.

Automating Contact Resolution

You can set up a conversation where agents work together to resolve issues. One agent gets the contact list using `list_contacts`. Another agent then decides which channel was used and calls `create_ticket` with all necessary context, ensuring no steps are missed.

Setting Up Communication Flows

Agents can coordinate complex setup tasks. One might check existing integrations using `list_webhooks`, while a second agent determines if new access is needed and calls `create_webhook` only if the first agent flags it as necessary for business continuity.

Setup guide

Set up Trengo MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Trengo tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Trengo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Trengo data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Trengo_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Trengo data")
    print(result.messages[-1].content)

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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 AutoGen

AutoGen uses the MCP Server tools as actions that agents can debate and decide upon. The framework allows you to build complex systems where multiple internal 'experts' discuss the best sequence of tool calls.

The core data is customer conversations across various channels, including messages and ticket status. Agents use tools like `list_messages` and `update_ticket` to manage this flow collaboratively.

Yes. One agent can call `list_team_members` to get the user roster, and another agent reviews that list to assign ownership to a newly created ticket via `create_ticket`, achieving consensus on assignment.

The server handles WhatsApp, email, chat, and social. Your agents can debate the best method for communication and execute it using `send_message`, ensuring the right channel is always used.

The server touches customer conversation data, including private messages. Because multiple agents are debating actions, you must carefully constrain which tools (like `get_ticket`) they can access to prevent unauthorized data exposure.

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