How to Use the Altoviz MCP in AutoGen

Q: How do I load the Altoviz tools into an AutoGen agent?

Install the autogen-extmcp package. Use mcpservertools with a StreamableHttpServerParams object pointing to your Vinkius URL.

Q: Do I need to format the JSON responses for the agents?

McpToolAdapter handles all schema conversions automatically. The agents read the raw API output as natural text in the chat history thanks to the MCP architecture.

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Connect Altoviz MCP to AutoGen

Create your Vinkius account to connect Altoviz 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 Altoviz with AutoGen

Ask AI about this MCP

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Perplexity

Ground AutoGen debates in facts

The `list_sale_quotes` and `get_sale_quote_details` MCP tools inject historical bids into the conversation. Agents arguing over pricing strategies need actual sales data to reach consensus. A sales agent proposes a discount, and a finance agent verifies past margins before agreeing. This dynamic prevents AI from inventing numbers. When the team needs to evaluate customer lifetime value, they call `get_customer_details`. Raw JSON output settles the debate immediately.

Audit receipts via MCP Server

One agent pulls recent transactions using `list_receipts` while another cross-checks the vendor using `list_suppliers`. Expense reconciliation requires multiple perspectives. They discuss discrepancies in the chat thread. A compliance bot flags unusual spending patterns based on the tool outputs. The negotiation continues until all agents sign off on the expense report. You watch the entire deliberation unfold in your terminal.

Coordinate product catalog updates

Triggering `list_products` gives both agents the same baseline reality. Inventory analysis often involves competing priorities. Marketing wants to push certain items, but operations cares about stock levels. They review the catalog entries together. If an item needs deeper inspection, one agent executes `get_product_details` and shares the findings. The final recommendation emerges from strict peer review.

Setup guide

Set up Altoviz 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 Altoviz 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="Altoviz_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the `autogen-ext[mcp]` package. Use `mcp_server_tools` with a `StreamableHttpServerParams` object pointing to your Vinkius URL.

Assign the tools to a specialized data-retrieval agent. Other agents in the chat will ask this specific bot to fetch invoices or customer lists.

The conversation flow dictates execution. Agents take turns speaking and calling tools, ensuring sequential access to the API.

`McpToolAdapter` handles all schema conversions automatically. The agents read the raw API output as natural text in the chat history thanks to the MCP architecture.

Vinkius manages the connection via stateless ephemeral sandboxes. Line items and tax totals from `get_sale_invoice_details` stream directly to your local environment. We retain absolutely nothing after the socket closes.

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