How to Use the Wuro MCP in AutoGen

Q: Does AutoGen require specific data types when accessing Wuro?

You need contact information (via listcontacts) and product identifiers (from listproducts). The agents use these inputs to achieve a consensus on the next required action.

Let multiple agents debate your Wuro billing needs using AutoGen's consensus framework.

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Create your Vinkius account to connect Wuro 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 Wuro with AutoGen

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Perplexity

Automate decision-making for invoicing.

You need to decide if a client qualifies for an immediate invoice or if they just need a quote first? AutoGen handles the debate. One agent can check `list_contacts` for status, while another checks `list_purchases`. They then negotiate whether the final output should be a `create_quote` request or a direct billing action. This is consensus-driven; it won't just execute the first command it sees. It forces agents to challenge assumptions until they agree on the best next step using the MCP Server tools.

Validate data before creating records.

The agents can debate data validity for you, preventing bad submissions. One agent might call `list_products` to check availability, while another calls `get_contact` to verify the client's current agreement terms. They only proceed when they reach consensus on whether a new billing action is appropriate. This prevents wasted API calls and ensures that any attempt to run `create_invoice` is thoroughly vetted by multiple simulated perspectives.

Coordinate full account onboarding.

Building a new account requires multiple steps—the agents manage the friction points. One agent handles calling `create_contact`, while another uses that new ID to immediately draft initial records via `get_quote`. This ensures all necessary prerequisites are met before any final billing action is taken through the MCP Server. The multi-agent setup makes it feel less like a sequence of calls and more like an internal team coordinating a complex project.

Setup guide

Set up Wuro 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 Wuro 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="Wuro_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

It simulates multiple agents debating the best path. If you call `create_contact`, one agent might check for necessary details using `list_employees` before giving a final 'OK' to proceed.

Yes, agents can debate discrepancies. If an invoice amount seems wrong based on the `get_invoice` details, one agent can flag it and force another agent to re-check the original product list using `list_products`.

The server provides full access to billing records. You're dealing with Quote details (`get_quote`), Invoice history, and the foundational Contact IDs needed to link all transactions.

You need contact information (via `list_contacts`) and product identifiers (from `list_products`). The agents use these inputs to achieve a consensus on the next required action.

Wuro exposes contact records, financial statements (invoices and quotes), and product catalogs. These are the core structured data sets the agents use to make their decisions.

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