How to Use the Bazaarvoice MCP in AutoGen

Q: How do AutoGen agents share Bazaarvoice data?

Agents pass the output of tools like getproduct back and forth in the conversation history. This allows them to build on each other's work.

Enable multi-agent debate using AutoGen and the Bazaarvoice MCP server for high-stakes customer support decisions.

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Connect Bazaarvoice MCP to AutoGen

Create your Vinkius account to connect Bazaarvoice 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 Bazaarvoice with AutoGen

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Debate Bazaarvoice data in AutoGen

Assign the `search_reviews` tool to one agent while another analyzes the output. You watch them argue over the findings until they reach a consensus on product quality. This setup prevents single-agent bias. You get a balanced view of customer sentiment by forcing agents to challenge each other.

Negotiate Bazaarvoice insights with AutoGen

Have a research agent pull `get_statistics` and a manager agent decide on the next move. They discuss the numbers and decide if more data is needed. This automated deliberation happens behind the scenes. Your agents use the tools to support their arguments during the conversation.

Execute Bazaarvoice actions via AutoGen

Let your agents invoke `list_answers` to resolve customer queries automatically. The conversation framework ensures that only verified answers get pushed. Your system builds a chain of command. Agents perform the research, debate the validity, and execute the tool call.

Setup guide

Set up Bazaarvoice 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 Bazaarvoice 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="Bazaarvoice_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Bazaarvoice 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 Bazaarvoice. 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 Bazaarvoice MCP in AutoGen

Agents pass the output of tools like `get_product` back and forth in the conversation history. This allows them to build on each other's work.

They can. You define the system prompts so that one agent acts as a critic, forcing the other to justify its interpretation of the review data.

No. The MCP adapter automatically converts the Bazaarvoice schema into function definitions that AutoGen understands natively.

The agents trigger the tools as part of their conversation flow. You control the logic by setting the agent's system instructions.

Your Bazaarvoice credentials stay inside the Vinkius sandbox. Only the agent gets the transient data it needs to perform its task.

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