How to Use the Arrivy MCP in AutoGen

Q: How do I update an Arrivy task from AutoGen?

Once your agents reach a consensus, they trigger the updatetask tool. This modifies the record directly in your account.

Let your AutoGen agents debate field assignments using the Arrivy MCP Server.

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Connect Arrivy MCP to AutoGen

Create your Vinkius account to connect Arrivy 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 Arrivy with AutoGen

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Debate Arrivy tasks in AutoGen

Set a security agent to check `get_task` details while a logistics agent proposes a schedule. They negotiate the best time for your team. This removes bias from your scheduling process. The agents arrive at a consensus that accounts for both service needs and crew availability.

Manage crews with AutoGen agents

Your agents use `list_crews` to see who is available. One agent might push for speed while another prioritizes experience level. They reach a final decision based on the current data in Arrivy. You get the best outcome without manual input.

Negotiate customer bookings in AutoGen

Agents query `list_customers` to verify account status before booking. If an agent flags a conflict, another proposes a resolution. This prevents scheduling errors. Your agents handle the back-and-forth so you don't have to.

Setup guide

Set up Arrivy 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 Arrivy 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="Arrivy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Arrivy MCP in AutoGen

You pass the tool list to your AssistantAgent. The agents will then call the relevant functions when they need to check or update field data.

Yes, by letting multiple agents debate the best course of action using the Arrivy API. They converge on the optimal task update.

It does. Each agent has the same access to your Arrivy data, allowing them to challenge each other's assumptions based on real-time numbers.

Once your agents reach a consensus, they trigger the `update_task` tool. This modifies the record directly in your account.

All agent interactions occur within our secure sandbox. Your task details are never exposed to external entities and remain isolated during the negotiation process.

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