How to Use the Clickatell MCP in AutoGen

Q: Can AutoGen use Clickatell to debate message content?

Absolutely. One agent writes the draft and another calls listwhatsapptemplates to ensure it matches your requirements.

Let AutoGen agents debate and execute Clickatell messaging tasks with multi-agent consensus.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Clickatell MCP to AutoGen

Create your Vinkius account to connect Clickatell to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Clickatell with AutoGen

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Debate message sends in AutoGen

Assign `send_whatsapp` to an executor agent. A reviewer agent checks the template requirements first to ensure compliance. This multi-agent flow forces deliberation. You avoid sending messages without proper validation.

Resolve account issues with AutoGen

One agent calls `get_account_balance` while another monitors the budget. They decide together if a bulk `send_bulk_messages` job should proceed. This logic prevents overspending. The agents negotiate based on the specific numbers returned.

Manage channel access via AutoGen

Agents query `list_active_channels` to verify connectivity. If a channel is missing, the agent triggers a configuration check. This automates your setup verification. Your agents keep the messaging infrastructure running smoothly.

Setup guide

Set up Clickatell 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 Clickatell 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="Clickatell_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Clickatell_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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