How to Use the European Pension Estimator MCP in AutoGen

Let specialized AutoGen agents debate European pension rules and calculate optimal retirement ages.

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Connect European Pension Estimator MCP to AutoGen

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Setup European Pension Estimator with AutoGen

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Resolve cross-border pension conflicts with AutoGen

Bottom line, cross-border retirement is a mess. This MCP gives your AutoGen agents the tools they need to debate complex financial scenarios. A French pension specialist agent can call `get_scheme_details` while a Spanish specialist agent does the same, allowing them to negotiate the best retirement strategy for a multi-national worker. They compare rules, retirement ages, and minimum contribution years. They work together to find the optimal path, presenting a unified, thoroughly debated plan to the user.

Debate benefit calculations between agents

Let's run the numbers. When projecting future income, one agent might be too conservative while another is too optimistic. This MCP exposes `calculate_monthly_benefit` to your financial analyst agent, who can present a baseline calculation that your risk-assessor agent can then stress-test. This collaborative process ensures that the final pension estimate accounts for potential policy changes or inflation adjustments. You get a realistic projection, not just a static best-case scenario.

Audit contribution gaps through multi-agent consensus

Missing years are a major pain point. This MCP lets your planning agent run `assess_contribution_gap` to find out if working longer actually pays off. Then, a tax agent can review that output to see if the extra income gets eaten up by higher tax brackets. This multi-agent review prevents costly mistakes. The agents negotiate until they reach a consensus on whether the client should buy back missing years or retire early.

Setup guide

Set up European Pension Estimator 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 European Pension Estimator 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="European Pension Estimator_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent European Pension Estimator 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="European Pension Estimator_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent European Pension Estimator 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 European Pension Estimator. 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 European Pension Estimator MCP in AutoGen

You register the MCP with your AssistantAgent using the provided adapter. The agents can then call the pension tools as needed during their multi-turn debate to resolve calculation discrepancies.

Yes. A critic agent can review the inputs and outputs of the pension calculations to ensure the planning agent isn't making unrealistic assumptions about retirement ages.

Yes. The tools return structured JSON data that agents can easily read, discuss, and pass to other agents in the conversation thread.

The agents use the pension tools to retrieve the exact rules for each country, then debate how the EU social security coordination rules apply to the combined contribution history.

Your contribution history and estimated benefits are kept strictly within your local execution environment. Vinkius manages the secure connection, and your private financial data is never stored.

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