How to Use the Deep Talk MCP in AutoGen

Coordinate multi-agent debates over your Deep Talk analytics in AutoGen.

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Connect Deep Talk MCP to AutoGen

Create your Vinkius account to connect Deep Talk 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 Deep Talk with AutoGen

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Perplexity

Debate conversation data in AutoGen

Have your analysts and security agents argue over the output of `list_conversation_clusters`. Each agent challenges the findings until they reach a consensus. This approach forces the system to verify findings before acting. You see the full deliberation process as the agents negotiate the meaning of the data.

Negotiate processing tasks in AutoGen

Use `list_processing_tasks` to feed status updates to your AutoGen team. If a job is slow, a performance agent can trigger a retry or flag it for review. This turns your monitoring into a dynamic process. Agents actively manage the data pipeline based on the real-time feedback from the API.

Verify sentiment with AutoGen agents

Assign one agent to call `get_sentiment_analytics` and another to critique the results. They check for anomalies in the distribution before finalizing any reports. This prevents single-agent bias. You get a balanced view of your customer sentiment through the collective decision-making of your agent group.

Setup guide

Set up Deep Talk 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 Deep Talk 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="Deep Talk_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Deep Talk 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="Deep Talk_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Deep Talk MCP in AutoGen

They can. You set up a group chat where agents use the MCP tools to gather data and then debate the results.

Install the extension and pass the tools to your AssistantAgent. The adapter maps the schemas so your agents can call them instantly.

Access is restricted to your specific endpoint token. No other process can view the metadata retrieved by your agents.

They only get the tools you explicitly include in the tool list. You maintain full control over what the agents can read or query.

The server handles multiple concurrent calls. Your AutoGen agents can trigger different tools simultaneously to speed up their deliberation.

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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