How to Use the Veraset MCP in AutoGen

Q: Can AutoGen handle multi-stage data analysis with Veraset?

Yes. The process can be: 1) Check available datasets (listmobilitydatasets). 2) Get schema definitions. 3) Run query. This sequence requires deliberation.

Drive consensus decisions using AutoGen across Veraset's data analytics tools.

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Connect Veraset MCP to AutoGen

Create your Vinkius account to connect Veraset 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 Veraset with AutoGen

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Debating Data Requirements with AutoGen

Set up two agents: one focused on speed and another on accuracy. Have them debate the best dataset to use by having them call `list_mobility_datasets` simultaneously, forcing consensus on the correct resource.

MCP Server for Decision Making with AutoGen

An agent can propose a query using `execute_sql_query`. A second agent reviews the proposed SQL syntax against `get_dataset_schema` before allowing the final execution, ensuring correctness.

Managing Data Flow in AutoGen

One agent retrieves metadata using `get_dataset_metadata`, and another uses that output to determine if a download link is needed. They negotiate which S3 folder prefix to check via `list_s3_delivery_folders`.

Setup guide

Set up Veraset 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 Veraset 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="Veraset_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You assign roles. One agent might query the data sample using `get_dataset_sample`, while another reviews that snippet to determine if it answers the user's core question.

Yes. The process can be: 1) Check available datasets (`list_mobility_datasets`). 2) Get schema definitions. 3) Run query. This sequence requires deliberation.

The agents can collaborate: one handles pagination logic for `get_query_results`, while another manages the final download link generation, preventing data overflow.

If two agents suggest different dataset IDs, you can enforce a check using `get_dataset_metadata` on both, letting the system compare the technical specifications and choose the right one.

The server handles mobility datasets and S3 folder names. Because multiple agents are involved, you need strict controls over which agent can call `generate_download_link`.

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