How to Use the Afosto MCP in AutoGen

Q: How do I register Afosto tools with AutoGen agents using MCP?

Install autogen-extmcp and use mcpservertools with your Vinkius URL. Pass the resulting tool list to your AssistantAgent constructor. The adapter takes care of mapping the schemas.

Deploy AutoGen agent teams to debate inventory levels and coordinate Afosto order fulfillment.

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Connect Afosto MCP to AutoGen

Create your Vinkius account to connect Afosto 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 Afosto with AutoGen

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Let agents debate stock allocations with this MCP Server

The `list_inventory` tool feeds real-time stock levels directly to your AutoGen agent conversation. One agent acts as a sales planner, pushing to clear inventory, while a logistics agent flags warehouse constraints. They use the live stock counts to negotiate the best distribution strategy. This consensus-driven approach prevents over-allocation of stock. The agents debate until they find an optimal balance based on the raw numbers. You get a reasoned decision instead of a simple rule-based output.

Automate order verification via agent consensus

The `list_orders` tool pulls multi-channel order data into your AutoGen multi-agent system. A fraud detection agent reviews the order details while a customer success agent checks the buyer's history. They compare notes to approve or flag high-risk transactions. You register the tools using `mcp_server_tools` and pass them to your `AssistantAgent`. The framework handles the schema conversion automatically. This lets your agents focus on the conversation rather than data formatting.

Match customer profiles to active product variants

The `list_customers` tool retrieves historical buyer behavior to inform your marketing agents. A creative agent drafts personalized offers, while a finance agent checks `list_products` to ensure the recommended variants hit margin targets. They iterate on the pitch until both agree it is profitable. This setup uses the streamable HTTP transport to keep agent conversations fast. By letting agents challenge each other's product selections, you avoid pitching low-margin or out-of-stock items to your best buyers.

Setup guide

Set up Afosto 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 Afosto 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="Afosto_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Afosto_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Afosto 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 Afosto. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Common questions about Afosto MCP in AutoGen

Install `autogen-ext[mcp]` and use `mcp_server_tools` with your Vinkius URL. Pass the resulting tool list to your `AssistantAgent` constructor. The adapter takes care of mapping the schemas.

Yes. Multiple agents in your conversation group can call tools like `list_products` or `list_inventory`. They share the same context, allowing them to debate the data in real-time.

Vinkius manages the authentication for you behind a single endpoint token. You do not need to hardcode API keys or manage sessions inside your AutoGen agent code.

The integration supports both stdio and Streamable HTTP transports. This lets you run your agent teams locally or deploy them to cloud environments.

Yes. All order details and customer records are fetched through an isolated, zero-trust connection managed by the MCP Server. The data is processed ephemerally, meaning your transaction histories are never stored or used to train public models.

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