How to Use the FieldAware MCP in AutoGen

Let your AutoGen agents debate dispatch schedules and invoice approvals using live FieldAware tools.

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Connect FieldAware MCP to AutoGen

Create your Vinkius account to connect FieldAware 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 FieldAware with AutoGen

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Debate dispatch schedules in AutoGen

With this MCP server, you can set up an AutoGen conversation where a dispatch agent proposes a schedule using `create_job` and reviews assignments by running `list_jobs`. Do not let a single agent make scheduling decisions in a vacuum. They debate the best routing, check the customer's history with `get_customer`, and only finalize the assignment once they agree. This multi-agent consensus prevents scheduling conflicts and keeps your technicians from getting burned out.

Automate invoice audits

Let your agents negotiate billing discrepancies using this MCP server to pull up invoices with `list_invoices` and `get_invoice`. A billing agent can analyze the costs while an auditor agent compares them against original estimates using `list_quotes`. If the numbers do not match, the agents flag the discrepancy and discuss the fix. They can check the exact parts used by running `list_items` to ensure the customer is only billed for what was actually delivered.

Coordinate asset maintenance

Keep your equipment running with automated checks that query `list_assets` to find the exact machine that needs service. One agent can monitor asset health, while another coordinates to schedule preventive maintenance before a critical failure occurs. Once they agree on the plan, the scheduling agent calls `create_job` to dispatch a technician. This collaborative approach ensures that no critical assets are missed and that maintenance is scheduled during low-impact hours.

Setup guide

Set up FieldAware 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 FieldAware 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="FieldAware_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Use the MCP tool adapter to convert the server's schema into AutoGen-compatible tools. Pass the resulting list to your AssistantAgent constructor so they can call tools like `list_jobs` during their conversations.

Yes. A dispatch agent can analyze customer requests, while a scheduling agent checks existing assignments with `list_jobs` and creates the new work order using `create_job` once they reach consensus.

You can set up a multi-agent chat where one agent reviews invoices using `list_invoices` and another checks quotes with `list_quotes`. They debate discrepancies and output a clean resolution summary.

No. Vinkius handles the connection using a single endpoint token. All agents in your AutoGen group share this secure connection to interact with the API.

Your contact lists and customer profiles are never exposed or stored. All tool executions run in isolated, ephemeral V8 sandboxes, keeping your sensitive business data secure and private.

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