How to Use the Lago MCP in AutoGen

Q: How do I configure the Lago MCP Server for my AutoGen agents?

Install autogen-extmcp. Use mcpservertools with your HTTP endpoint to expose the billing tools.

Q: Can multiple AutoGen agents use the Lago tools at once?

You pass the tool list to the AssistantAgent constructor. Any agent with those permissions can propose or execute a billing change.

Q: Do I need to manually convert the Lago schemas for AutoGen?

No. The McpToolAdapter handles the schema conversion automatically so your agents understand the required parameters.

Let your AutoGen agents debate billing changes before they happen. Connect Lago to multi-agent teams that negotiate pricing structures.

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Perplexity

AutoGen MCP Server Workflows

The `create_billable_metric` and `create_plan` tools allow your multi-agent team to design and deploy complex pricing models. A product agent proposes a new metric, while a finance agent challenges the profitability of the plan. They argue back and forth over the parameters until they reach a consensus. Once both agents agree, the system executes the tool call to push the finalized plan directly to Lago.

Wallet Provisioning

Use the `create_wallet` and `apply_coupon` tools to build automated discount approval systems. When a customer requests a credit, a support agent flags the request, and a manager agent reviews their historical spend. The manager agent approves the transaction and triggers the coupon application. You get a strict, multi-perspective review process for every dollar of promotional credit issued.

Account Reconciliation

The `upsert_customer` and `get_customer` tools let your agents verify account details across systems. An auditing agent pulls the external ID from your CRM and cross-checks it against the Lago database. If the records mismatch, the agents discuss the discrepancy and decide which system holds the correct data. The MCP integration then pushes the correction, keeping your billing profiles perfectly synced.

Setup guide

Set up Lago 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 Lago 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="Lago_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with your HTTP endpoint to expose the billing tools.

You pass the tool list to the `AssistantAgent` constructor. Any agent with those permissions can propose or execute a billing change.

No. The `McpToolAdapter` handles the schema conversion automatically so your agents understand the required parameters.

You use either stdio or Streamable HTTP transports depending on your infrastructure requirements.

Your agents only access the specific wallet IDs and credit amounts required for the transaction. The architecture enforces strict zero-trust boundaries, meaning unauthorized agents cannot reach the isolated memory space.

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