How to Use the SurveySparrow MCP in AutoGen

Q: What happens when AutoGen Agents need to validate survey goals?

The agents can use listsurveys to review all available templates. They then debate which template best fits the current objective, ensuring alignment across different teams and departments.

Q: How does SurveySparrow support AutoGen Agents in data analysis?

The agents can call listsurveyresponses to get all submissions. They then debate how to segment this raw list, perhaps filtering by the contact folders retrieved using listcontactfolders.

Q: Which types of data does SurveySparrow handle regarding privacy when used with AutoGen?

The system handles sensitive account details via getuserprofile. The agents' debate process can be configured to require explicit confirmation before any action that modifies user contact or survey settings.

Designing Debate-Driven Workflows for AutoGen Agents.

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Debating the Ideal Contact List

When multiple agents need to decide who to survey, they can debate using `list_crm_contacts`. One agent might suggest a large list; another can use `get_contact_info` to flag accounts that are inactive or belong to restricted folders (`list_contact_folders`). The final consensus output is the vetted, agreed-upon audience for your campaign.

Negotiating Survey Structure with AutoGen

An agent can propose a survey topic using `list_surveys`. A second agent critiques this proposal by calling `get_survey_questions` to identify gaps or redundant fields. They negotiate the final, optimal structure before any deployment happens. The debate ensures that the resulting survey is robust and logically sound.

Consensus on Survey Results Analysis

After submissions come in, one agent calls `list_survey_responses` to gather data. A second agent then uses `get_user_profile` to contextualize that feedback against the user's account history. They debate what findings are most critical and actionable for your team. This structured deliberation prevents basing decisions on incomplete or unverified metrics.

Setup guide

Set up SurveySparrow 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 SurveySparrow 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="SurveySparrow_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Agents can collaboratively check existing setups by running `list_surveys` and comparing the results against historical data pulled from `list_email_shares`. This ensures all campaign components are aligned before launch.

Yes. If an agent determines a new target is needed, it can first check for existing records via `get_contact_info`, and only proceed with `create_contact` if the necessary data is missing.

The agents can use `list_surveys` to review all available templates. They then debate which template best fits the current objective, ensuring alignment across different teams and departments.

The agents can call `list_survey_responses` to get all submissions. They then debate how to segment this raw list, perhaps filtering by the contact folders retrieved using `list_contact_folders`.

The system handles sensitive account details via `get_user_profile`. The agents' debate process can be configured to require explicit confirmation before any action that modifies user contact or survey settings.

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