How to Use the Uniconta MCP in AutoGen

Q: Can AutoGen handle complex GL account analysis in Uniconta?

Yes. You can build a system where one agent calls listglaccounts and another checks related daily journals using listdailyjournals. They debate the correct classification of a transaction, giving you a reasoned conclusion.

Systematic ERP Problem Solving with AutoGen. Get consensus on Uniconta data decisions through multi-agent debate.

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Connect Uniconta MCP to AutoGen

Create your Vinkius account to connect Uniconta 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 Uniconta with AutoGen

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Perplexity

Consensus on financial reporting

You can set up a debate where one agent retrieves the GL accounts using `list_gl_accounts`, and another checks the daily journals with `list_daily_journals`. The agents then discuss if the recorded transactions match the expected account structure. The resulting decision isn't just data; it's an agreed-upon conclusion reached by multiple perspectives, simulating a true internal review process.

Verifying supplier and customer records

Set up agents to challenge each other: one agent calls `list_creditors` for vendor status while another calls `get_debtor_details` for debtor compliance. They debate if the necessary documentation is present before approving a payment. This process ensures that critical business decisions are vetted from multiple viewpoints, making the final output highly reliable.

Inventory and Employee Workflow Validation

Use AutoGen to simulate an approval workflow. Agent A gets the list of items using `list_inventory_items`, and Agent B checks if the required inventory staff exist via `list_employees`. They then negotiate whether the item can be sold or manufactured. The final output is a documented consensus on feasibility, not just a data dump.

Setup guide

Set up Uniconta 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 Uniconta 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="Uniconta_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen allows agents to debate decisions based on the results of MCP calls. For example, two agents can argue over whether a debtor is valid after one agent runs `list_debtors` and another runs `get_debtor_details`. The final answer requires consensus.

Yes. You can build a system where one agent calls `list_gl_accounts` and another checks related daily journals using `list_daily_journals`. They debate the correct classification of a transaction, giving you a reasoned conclusion.

You can assign agents to specifically review vendor records. One agent calls `list_creditors`, and another checks for specific employee approvals (`list_employees`), requiring both sets of data before concluding the process.

Absolutely. You can build a pipeline where agents verify inventory availability (`list_inventory_items`) and then check the debtor's standing (`get_debtor_details`), only confirming the sale if all conditions are met.

This server handles core financial and operational records. Specifically, it touches Account Records (from `list_gl_accounts`), Customer Data (from `list_debtors`), and Invoice Records (from `list_debtor_invoices`).

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