How to Use the Smarty MCP in AutoGen

Achieve consensus decisions with Smarty for AutoGen.

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Connect Smarty MCP to AutoGen

Create your Vinkius account to connect Smarty to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Smarty with AutoGen

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Debating Address Validity

Set up conflicting agents to validate an address. One agent calls `autocomplete_us_address` and suggests a location, while another agent runs `validate_us_address`. They debate the findings until they converge on a final DPV status. This simulates real-world disagreement.

Negotiating Data Completeness

Agents can negotiate required data points. One agent might run `extract_addresses` on a raw email, finding an address. A second agent then challenges it by running `validate_intl_address`, forcing the first agent to confirm or correct missing details like country codes.

Multi-Layered Data Checks

You can force agents to check multiple sources before answering. One agent checks the address via `verify_zip_code` for local consistency, and a second agent runs `get_account_info` to confirm if the user has permission to use that location data. It’s a full deliberation process.

Setup guide

Set up Smarty 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 Smarty 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="Smarty_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Smarty provides objective, verifiable facts about locations. Instead of agents guessing, they debate the concrete results from tools like `validate_us_address`, leading to consensus decisions based on data.

Yes. You can set up a multi-agent conversation where one agent predicts an international address using `autocomplete_intl_address` and another validates it using `validate_intl_address`, ensuring robust global coverage.

Absolutely. You can pit agents against each other on a single address, having one suggest the location and another verifying the DPV status using `validate_us_address`. It’s a perfect debate scenario.

It provides structured validation outputs (coordinates, status, components). This objective data acts as the core evidence that competing agents use to reach a shared conclusion.

The server touches physical address data (US and International). The results of these location tools are used as evidence during the deliberation process, keeping the input and output streams controlled.

Use it with your favorite AI tools

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