How to Use the MRPLN MCP in AutoGen

Q: Can I set up a QA agent in AutoGen to verify MRPLN customer data?

Yes, it's a perfect use case. One agent calls createcustomer or updatecustomer, and a second verification agent is triggered to call getcustomer and confirm the data integrity. The conversation between them serves as a log of the change.

Q: What's an example of AutoGen agents debating a notification strategy using MRPLN?

A 'FloorManager' agent might suggest a mass sendsmsmessage about a delay. A 'Support' agent could then use getcustomer to argue for a more targeted approach, and they would negotiate a final plan before any messages are sent.

Q: How are the MRPLN tools exposed to agents in an AutoGen conversation?

You use the mcpservertools function from the AutoGen extension to fetch the tools. Then you register that list of tools with your AssistantAgent. The framework handles making the tools available in the agent conversation.

Let your AutoGen agents debate the best production and outreach strategies for MRPLN.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect MRPLN MCP to AutoGen

Create your Vinkius account to connect MRPLN 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.

GDPR Free for Subscribers

Setup MRPLN with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Let Agents Debate the Next Best Action

This isn't about one agent executing a plan. It's about multiple agents reaching a consensus. You can have a 'Marketing' agent propose a new campaign by analyzing the output of `list_tactics`. A 'Finance' agent can immediately challenge it, using `get_tactic_performance` to argue about the cost and expected return. They discuss the options, and you supervise until they agree on the right message to send with `send_email_message`.

Negotiate Production Alerts

Avoid knee-jerk reactions to production problems. When a delay occurs, a 'ProductionManager' agent might propose using `send_sms_message` to alert all affected clients immediately. This is where AutoGen shines. A 'CustomerSuccess' agent can intervene, check `get_customer` details, and argue that high-value clients deserve a personal call, not a generic text. The agents negotiate a segmented communication plan, using the MRPLN tools to inform their debate.

Verify Customer Data with AutoGen

Use a multi-agent conversation to keep your data clean. Set up a simple workflow where one agent's job is to `create_customer` whenever a new lead comes in from a form. That's its only job. Then, a second 'QA' agent automatically wakes up. It calls `get_customer` with the ID from the first agent, checks the data for formatting errors, and cross-references `list_customers` to prevent duplicates. This conversational check-and-balance catches errors before they pollute your database.

Setup guide

Set up MRPLN 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 MRPLN 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="MRPLN_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent MRPLN 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 MRPLN. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect MRPLN now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about MRPLN MCP in AutoGen

You'd assign different roles to your agents. One agent could use `list_tactics` to propose ideas, while another uses `get_tactic_performance` to play devil's advocate and critique the cost-effectiveness, leading to a better final decision.

Yes, it's a perfect use case. One agent calls `create_customer` or `update_customer`, and a second verification agent is triggered to call `get_customer` and confirm the data integrity. The conversation between them serves as a log of the change.

A 'FloorManager' agent might suggest a mass `send_sms_message` about a delay. A 'Support' agent could then use `get_customer` to argue for a more targeted approach, and they would negotiate a final plan before any messages are sent.

You use the `mcp_server_tools` function from the AutoGen extension to fetch the tools. Then you register that list of tools with your `AssistantAgent`. The framework handles making the tools available in the agent conversation.

Each agent's access to customer details and message templates is isolated within the tool call itself. The Vinkius platform ensures every call is executed in a dedicated sandbox. Even though multiple agents are in the conversation, they all operate through a single, secure MCP endpoint token.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript