How to Use the WSLA (WhatsApp) MCP in AutoGen

Achieve consensus on WhatsApp messaging with your AI client and AutoGen.

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Connect WSLA (WhatsApp) MCP to AutoGen

Create your Vinkius account to connect WSLA (WhatsApp) 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 WSLA (WhatsApp) with AutoGen

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Perplexity

Debating message approach

Agents debate the best way to communicate. If they need quick confirmation, one agent calls `send_whatsapp_text` to execute the message. The other agents then review that output before deciding on further actions. This consensus-driven flow ensures that simple messaging is vetted by multiple perspectives.

Comparing template vs. media details

A security agent might challenge if a template is necessary, forcing another agent to use `list_whatsapp_templates` first. Meanwhile, an expert agent uses `get_whatsapp_media_details` and presents that data to the group for final approval. This forces deliberation between structural constraints (templates) and raw data context (media).

Orchestrating complex flows

The system can handle multi-step decisioning. Agents might first draft a message using `send_whatsapp_template`. Then, they debate if a simple reaction is needed. If consensus dictates it, the final step calls `send_whatsapp_reaction` to complete the interaction on WhatsApp. It's about achieving agreement on every single tool call.

Setup guide

Set up WSLA (WhatsApp) 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 WSLA (WhatsApp) 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="WSLA (WhatsApp)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent WSLA (WhatsApp) 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="WSLA (WhatsApp)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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 WSLA (WhatsApp). 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 WSLA (WhatsApp) MCP in AutoGen

AutoGen uses its debate framework to determine the best sequence of calls. The agents discuss whether a simple text message is better than a template, ensuring the final action makes sense.

Yes. You can set up an agent group where one agent reviews available options using `list_whatsapp_templates` before another commits to sending a structured message via `send_whatsapp_template`.

The agents can debate if an acknowledgement is needed, and if consensus is reached, they execute the action using `send_whatsapp_reaction`. This makes reactions part of a larger deliberation process.

This MCP Server provides message content (text/templates), media metadata, and user reactions. AutoGen's strength is using these diverse outputs to resolve conflicting requirements among its agents.

The exact data type this server touches that affects privacy is message content and media metadata. The debate structure helps verify that the right data is used at every step.

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