How to Use the Alegra MCP in AutoGen

Q: What's the setup process for using Alegra tools with AutoGen?

Install autogen-extmcp and use the mcpservertools function with your Vinkius server URL. This function returns a list of tools that you can directly pass into your AssistantAgent configuration. AutoGen handles the rest.

Q: Can one AutoGen agent check inventory in Alegra while another creates an invoice?

Exactly. This is the core use case. One agent calls listinventoryitems to confirm stock. It then passes the result to another agent in the chat, which decides whether to proceed with createinvoice.

Let a team of AutoGen agents debate and manage your Alegra account. Automate complex invoicing and inventory decisions through conversation.

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Setup Alegra with AutoGen

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A Team of Agents to Manage Your ERP

Stop writing rigid scripts. With AutoGen, you can assign different roles to a group of agents to manage Alegra. An 'Accountant' agent can use `list_invoices` and `list_payments` to track finances. A 'Sales' agent can use `list_estimates` to follow up on quotes. These agents don't just act; they talk to each other. The Sales agent might ask the 'Inventory' agent to `list_inventory_items` to check stock before finalizing an estimate. This conversational approach handles complex, multi-step tasks that require coordination.

Debate Financial Decisions Before Acting

The real advantage of AutoGen is consensus. One agent might propose creating a large invoice using `create_invoice`. A 'Compliance' agent can then review the proposal, use `get_contact_details` to check the client's status, and flag any potential issues before the invoice is actually created. This prevents mistakes. You can build a system where one agent drafts an action, another critiques it, and a human user gives the final approval. It's a safer way to automate changes to your financial records in Alegra.

Your Alegra MCP Server for Multi-Agent Systems

The Alegra tools are designed for agent interaction. An agent can call `get_invoice_details`, and another agent can analyze the output to decide the next step. The `autogen-ext[mcp]` package makes it simple to give these tools to any `AssistantAgent`. Imagine a scenario: one agent detects a new entry from `list_contacts`. It alerts another agent, which then runs `create_invoice` to generate a welcome package invoice. This is how you build robust, event-driven systems with AutoGen and your live Alegra data.

Setup guide

Set up Alegra 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 Alegra 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="Alegra_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Alegra MCP in AutoGen

You can have a 'Watcher' agent that looks for new contacts via `list_contacts`. When it finds one, it tells an 'Onboarding' agent to `create_invoice` for a setup fee and maybe `create_estimate` for future work.

Yes, that's the point of this MCP server. You can create agents that specialize in certain tasks, like an 'Auditor' agent that only has permission to use `list_invoices` and `list_payments` to review financial history.

Install `autogen-ext[mcp]` and use the `mcp_server_tools` function with your Vinkius server URL. This function returns a list of tools that you can directly pass into your `AssistantAgent` configuration. AutoGen handles the rest.

Exactly. This is the core use case. One agent calls `list_inventory_items` to confirm stock. It then passes the result to another agent in the chat, which decides whether to proceed with `create_invoice`.

Your agents access Alegra's invoice, contact, and inventory data through the MCP server. Each tool call is an independent, authenticated request processed in a Vinkius sandbox. The conversational data between agents happens within your AutoGen environment, while the actual data fetching is secured end-to-end.

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