How to Use the DataScope MCP in AutoGen

Let your AutoGen agents debate field data and reach consensus on audit results.

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Connect DataScope MCP to AutoGen

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Setup DataScope with AutoGen

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Perplexity

Debate DataScope forms in AutoGen

Assign one agent to `list_form_submissions` while another verifies the data quality. They argue until they agree on the audit status. This stops bad data from passing. The agents challenge each other based on the output from your inspection forms.

Coordinate teams with AutoGen

Use `list_organization_users` to let your agents decide who handles a specific task. They negotiate assignments based on current availability. Your agents handle the logistics. They look at the user list and reach a consensus on the best person for the job.

Check field locations via AutoGen

The agents use `list_tracked_locations` to confirm site attendance. If the locations don't match the audit, the agents flag the discrepancy. They don't just report data. They analyze it and resolve conflicts between different sources.

Setup guide

Set up DataScope 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 DataScope 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="DataScope_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Pass the tool list from the server to your AssistantAgent constructor. The adapter handles the schema so agents can call them immediately.

Yes, agents pass tool outputs in their conversation history. They can challenge or confirm data found in any form submission.

All traffic is restricted to your local instance. Your agent interactions are private and isolated from external networks.

They automatically decide which tool to call based on your task description. You define the goal, and they manage the execution flow.

No. We only provide the server connection. Your inspection data and agent debates stay entirely within your control.

Use it with your favorite AI tools

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