How to Use the European Pension Estimator MCP in LlamaIndex

Index European pension rules and calculate benefits directly within your LlamaIndex RAG pipelines.

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Connect European Pension Estimator MCP to LlamaIndex

Create your Vinkius account to connect European Pension Estimator to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup European Pension Estimator with LlamaIndex

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Build a searchable European pension knowledge base

Stop letting static PDFs dictate your retirement advisory. This MCP lets you index real-time regulatory structures directly into your vector store. By calling `get_scheme_details`, your LlamaIndex pipeline pulls official rules for Germany, France, Spain, and the UK, turning raw policy into searchable, structured knowledge. Your agent queries this index to answer complex cross-border questions. You avoid hallucinations because the answers are grounded in actual statutory data, not outdated training sets.

Ground pension calculations in actual data

Right, here is the deal. If you want to know what someone will actually receive, you need to run the formulas. This MCP exposes `calculate_monthly_benefit` to your index, allowing the agent to run live calculations and save the results directly into the user's profile index. This creates a persistent record of the user's projected financial state. You can query past calculations to see how changing assumptions affect the final retirement income over time.

Analyze contribution gaps using LlamaIndex

The math doesn't lie. When a client has gaps in their employment history, you need to know the cost. This MCP enables your pipeline to use `assess_contribution_gap` to fetch the precise benefit increase of working additional years, then indexes that analysis. This allows your financial agent to retrieve historical gap analyses instantly. It helps you compare different retirement scenarios side-by-side using semantic search.

Setup guide

Set up European Pension Estimator MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all European Pension Estimator MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to European Pension Estimator tools.",
)
response = await agent.run("List recent European Pension Estimator data")

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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 LlamaIndex

Yes. You can run the pension tools and index the resulting calculations directly into Qdrant, Pinecone, or any other supported vector store for semantic retrieval.

The agent relies on the live outputs of the pension tools, meaning its answers are strictly bounded by the actual statutory formulas retrieved during the query.

No. The MCP is stateless, but you can store the generated pension estimates in your LlamaIndex document store to maintain context across different user sessions.

Yes. Your agent can query multiple countries and index the comparative analysis, allowing users to ask natural language questions about which country offers better retirement terms.

Your local earnings history and contribution months are processed locally within your pipeline. Vinkius encrypts the transport layer, ensuring no raw financial records are ever intercepted.

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