How to Use the GetFeedback MCP in AutoGen

Q: How do I configure the GetFeedback MCP Server for AutoGen?

Install the autogen-extmcp package. Use mcpservertools with your endpoint URL to fetch the tools, then pass that list directly to your AssistantAgent constructor.

Q: How do AutoGen agents handle GetFeedback pagination?

They negotiate the process. One agent calls listfeedbackpage and passes the resulting cursor to another agent, which decides whether to request the next page or stop based on the current context.

Build debating AutoGen agents that manage your feedback pipelines.

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Connect GetFeedback MCP to AutoGen

Create your Vinkius account to connect GetFeedback 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 GetFeedback with AutoGen

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Coordinate GetFeedback MCP Server Tasks

The GetFeedback MCP Server gives your AutoGen agents the tools they need to argue over customer data. You assign `list_survey_responses` to an analyst agent. It pulls the raw feedback while a separate critic agent reviews the findings, challenging the analyst's interpretation of the data. They do not stop at reading text. A manager agent can review the debate, decide that more data is necessary, and instruct a deployment agent to execute `send_survey_invites`. The agents negotiate the targeting criteria before a single email goes out.

Debate API Resource Allocation

Hitting rate limits crashes pipelines. You give an infrastructure agent access to `check_api_limits` and `verify_api_connection`. This agent monitors the connection health and warns the rest of the swarm if they are pulling data too aggressively. If the limit approaches zero, the infrastructure agent vetoes further data requests. The data agents must then rely on `list_recent_feedback` for smaller, targeted pulls instead of demanding massive historical downloads via pagination.

Cross-Examine Survey Metrics

Agents verify claims using hard data. If one agent proposes a product change based on a few complaints, a statistics agent runs `get_survey_stats` to check the actual response volume. They compare the anecdotal feedback against the overall completion rate. The agents drill down into specific anomalies using `get_response_details`. They pull the exact metadata for outlier responses, debate whether the user profile matches your core demographic, and reach a consensus before generating a final report.

Setup guide

Set up GetFeedback 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 GetFeedback 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="GetFeedback_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the autogen-ext[mcp] package. Use mcp_server_tools with your endpoint URL to fetch the tools, then pass that list directly to your AssistantAgent constructor.

Yes. You can distribute the tools across different agents. One agent gets read-only access via list_surveys, while a restricted manager agent holds the keys to send_survey_invites.

They negotiate the process. One agent calls list_feedback_page and passes the resulting cursor to another agent, which decides whether to request the next page or stop based on the current context.

The agent receives the error payload and attempts to fix it. If get_survey_details fails due to a bad ID, the agent will typically ask another agent to verify the ID using the list endpoint.

The server accesses raw survey answers and completion timestamps. Vinkius requires only one endpoint token to authenticate. The network traffic routes through an isolated sandbox that drops all state the moment your agents finish their conversation.

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