How to Use the Fieldly MCP in AutoGen

Q: How do I register Fieldly MCP tools with my AutoGen agents?

Install autogen-extmcp and use mcpservertools with your Vinkius URL. Pass the resulting tool list directly to your AutoGen AssistantAgent during initialization.

Q: How do AutoGen agents handle Fieldly customer records?

Your agents can call listcustomers to verify client details during their debate. This ensures they are matching the correct customer ID to new work items.

Let your AutoGen agents debate and coordinate Fieldly construction tasks and scheduling.

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Connect Fieldly MCP to AutoGen

Create your Vinkius account to connect Fieldly 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 Fieldly with AutoGen

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Debate project scheduling with AutoGen

The `list_bookings` tool allows your AutoGen agents to review current field schedules and debate resource allocation. A scheduling agent can propose a new booking, while a supervisor agent checks `list_users` to see if the crew is actually available. This consensus-driven approach stops scheduling conflicts before they happen. The agents negotiate until they find a slot that works. Once they agree, the execution agent calls `create_work_item` to lock in the job details on the shared calendar.

Check billing accuracy using AutoGen and this MCP Server

The `list_invoices` tool lets a specialized accounting agent pull financial records for review. In an AutoGen group chat, this financial agent can cross-reference invoice line items against jobs pulled via `list_work_items`. Another agent can then flag discrepancies before finalizing the bill. This multi-agent review process ensures your billing matches the actual work done on-site. The agents query `get_invoice` to inspect specific charges and reach a consensus before updating the customer.

Coordinate inventory levels across teams

The `list_articles` tool gives your procurement agents real-time access to your inventory and service articles. When a new project is proposed, one agent checks material availability while another compares it against the job requirements. This prevents starting a job without the necessary supplies. If stock is low, the agents can coordinate to flag the issue. They use `get_me` to check user permissions before suggesting changes to the inventory list.

Setup guide

Set up Fieldly 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 Fieldly 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="Fieldly_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Fieldly_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Install `autogen-ext[mcp]` and use `mcp_server_tools` with your Vinkius URL. Pass the resulting tool list directly to your AutoGen `AssistantAgent` during initialization.

Yes, after debating and agreeing on scheduling, an agent can invoke `create_work_item` to write the new job directly to your Fieldly database.

Your agents can call `list_customers` to verify client details during their debate. This ensures they are matching the correct customer ID to new work items.

Yes, AutoGen's MCP adapter natively supports both stdio and Streamable HTTP transports, allowing it to connect to your Vinkius-hosted MCP endpoint without extra configuration.

Your customer records and invoices remain completely private. Vinkius isolates the Fieldly MCP server in a zero-trust sandbox, ensuring that sensitive data is only processed within your local multi-agent conversation.

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