How to Use the Anaplan MCP in AutoGen

Deploy multi-agent teams in AutoGen to debate and execute your Anaplan data strategies.

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Connect Anaplan MCP to AutoGen

Create your Vinkius account to connect Anaplan 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 Anaplan with AutoGen

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Perplexity

Debating Anaplan operations in AutoGen

Configure one agent to monitor `get_task_status` while another agent prepares the next `run_import`. They communicate to resolve conflicts before taking action. This setup allows agents to challenge each other on whether a process should proceed. It creates a consensus-driven approach to your data management.

Automated workspace discovery for AutoGen

Your agent team uses `list_workspaces` to map out the environment. They negotiate which workspace to target based on performance and availability metrics. Once they agree, they use `list_models` to identify the correct container. The agents ensure they are working on the right target before any data changes occur.

Secure process orchestration

One agent acts as a validator, checking the output of `list_processes` against your compliance rules. Only if it approves does the execution agent run the command. This division of labor prevents unauthorized or accidental data triggers. You get a record of the debate that led to each action.

Setup guide

Set up Anaplan 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 Anaplan 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="Anaplan_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Anaplan MCP in AutoGen

They use a shared tool list to discuss and execute actions. You define the roles, and the agents handle the coordination between tool calls.

Absolutely, one agent can initiate a process while another monitors its status. They can even decide to halt or retry based on the feedback they receive.

You gain the ability to have agents debate critical data updates. This adds a layer of human-like oversight to your automated planning workflows.

The adapter handles the schema conversion for you. You just pass the server tools into your agent constructor and define the conversation flow.

We use a zero-trust architecture. All interactions between your agents and the server are scoped to the specific task and cleared immediately after completion.

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