How to Use the 8x8 MCP in AutoGen

Let your AutoGen agents debate and execute 8x8 communication tasks in a multi-agent system.

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Connect 8x8 MCP to AutoGen

Create your Vinkius account to connect 8x8 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 8x8 with AutoGen

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Debate 8x8 actions in AutoGen

Assign one agent to monitor `get_call_status` and another to manage `send_voice_message`. They discuss the best path before taking action. This consensus model prevents errors. The agents check each other's work, ensuring that messages only go out when conditions are met.

Manage 8x8 sub-accounts in AutoGen

Use a specialized agent to `list_sub_accounts` and audit activity. If an account hits a limit, the agent negotiates a response strategy. This is ideal for complex environments. You build systems where agents handle the heavy lifting of account management through constant deliberation.

Automate 8x8 messaging via AutoGen

Configure an agent to use `send_sms` or `send_chat_message` based on team feedback. The system verifies the message content before hitting the API. This adds a layer of human-like review to your automated flows. Agents challenge the request, verify the logic, and only then perform the action.

Setup guide

Set up 8x8 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 8x8 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="8x8_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent 8x8 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="8x8_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent 8x8 data")
    print(result.messages[-1].content)

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Common questions about 8x8 MCP in AutoGen

Pass the tool list to your AssistantAgent. The adapter handles the schema conversion, so your agents can call tools natively.

Agents can debate the error response. If a message fails, they decide whether to retry or escalate the issue to another agent.

Vinkius uses ephemeral sessions. Your communication data is processed in a secure, isolated sandbox that terminates when the task ends.

Yes, agents share the conversation history. One agent can read the balance and the next can decide how to spend it based on that info.

They only access what you allow through the tool set. You control exactly which account records they can see and modify.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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