How to Use the Guestmeter MCP in AutoGen

Q: Can AutoGen agents collaborate to resolve Guestmeter detractor issues?

Yes. One agent gathers detractor profiles via listdetractors while another drafts the recovery messages, coordinating their actions in a shared conversation.

Q: How does AutoGen handle MCP survey dispatching errors?

If the sendsurvey tool returns an error, the executing agent reports it to the group. The coordinator agent then decides whether to retry or escalate the issue to a human.

Q: What parameters does the survey tool require?

The sendsurvey tool requires either a valid email address or a phone number to deliver the feedback request.

Run multi-agent debates in AutoGen using this MCP Server to analyze Guestmeter feedback.

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Create your Vinkius account to connect Guestmeter 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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Escalation debates via AutoGen

The `list_detractors` tool pulls a list of unhappy guests into your AutoGen conversation. A customer service agent and a manager agent debate the best recovery option based on the guest's history. Once they agree on a solution, they use `get_guest_details` to verify the contact information. This ensures your team only acts after a consensus is reached, preventing conflicting messages.

Smart survey dispatch

The `send_survey` tool dispatches automated emails or text messages to guests after checkout. Inside AutoGen, a scheduling agent and a content agent coordinate to choose the right time and channel. They monitor responses using `list_guest_feedback` to see who replied. If a guest remains silent, the agents discuss whether to switch channels or stop outreach to avoid spamming.

Real-time feedback triage

The `list_recent_feedback` tool retrieves the 50 latest reviews for your AutoGen agents to analyze. One agent flags urgent issues while another filters out positive comments using `list_promoters`. They collaborate to assign tasks to your physical staff. This turns raw survey data into organized action items without human intervention.

Setup guide

Set up Guestmeter 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 Guestmeter 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="Guestmeter_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Guestmeter. 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 Guestmeter MCP in AutoGen

Use the autogen-ext package to load the tools via the Streamable HTTP transport. Pass the adapted tools directly into your AssistantAgent constructor.

Yes. One agent gathers detractor profiles via `list_detractors` while another drafts the recovery messages, coordinating their actions in a shared conversation.

If the `send_survey` tool returns an error, the executing agent reports it to the group. The coordinator agent then decides whether to retry or escalate the issue to a human.

The `send_survey` tool requires either a valid email address or a phone number to deliver the feedback request.

The Vinkius platform processes guest ratings and feedback using ephemeral, zero-trust isolates. Your data is never stored on Vinkius servers or used for model training.

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