How to Use the ArborNote MCP in AutoGen

Let AutoGen agents debate and coordinate ArborNote tree inventory schedules and client proposals for optimal field dispatch.

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Connect ArborNote MCP to AutoGen

Create your Vinkius account to connect ArborNote 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 ArborNote with AutoGen

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Multi-Agent Debate on Arborist Crew Schedules

The `list_arbornote_schedules` tool feeds active crew assignments into your AutoGen multi-agent conversation. A dispatch agent proposes a new tree survey route, while a budget agent analyzes the metrics to ensure the trip is cost-effective. They debate the schedule adjustments until they reach a consensus. Once agreed, the execution agent calls `update_arbornote_client` to finalize the appointment details in your system.

Automated Proposal Audits via AutoGen MCP Server

The `list_arbornote_proposals` tool lets your specialized AutoGen agents review bids before they go to clients. This MCP Server allows a risk-assessment agent to check tree inventory data while a sales agent reviews pricing. These agents cross-reference the proposal details with `get_arbornote_project` to verify that all hazardous trees are accounted for. The final output is an audited, risk-free proposal ready for client signature.

Multi-Agent Client Onboarding and Verification

The `create_arbornote_client` tool registers new accounts after your AutoGen agents verify their details. A validation agent checks the input formatting, while a CRM agent checks if the client already exists using `list_arbornote_clients`. This collaborative validation prevents duplicate records in your forestry database. The agents only execute the creation tool once they both agree the data is clean.

Setup guide

Set up ArborNote 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 ArborNote 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="ArborNote_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You initialize the streamable HTTP server parameters with your Vinkius URL. Then, pass the adapter-wrapped tools directly to your AutoGen AssistantAgent.

Yes. Based on the conversation flow, your agents decide when to call `get_arbornote_metrics` or `list_arbornote_projects` to resolve their debate.

They discuss the discrepancy in the chat thread. For example, if a proposal doesn't match the project details returned by `get_arbornote_project`, they flag it for human review.

It uses the Streamable HTTP transport provided by the Vinkius MCP platform. This ensures rapid, secure tool execution during agent conversations.

Vinkius processes all MCP tool calls in zero-trust, ephemeral V8 isolates. Your financial metrics and client contacts are processed in memory and never written to persistent storage.

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