How to Use the European Pension Estimator MCP in CrewAI

Q: Can I limit which European Pension Estimator tools a specific CrewAI agent can use?

Yes. For more control, you can use MCPServerHTTP from crewai.mcp and provide a toolfilter. This lets you expose only the specific tools that a particular agent's role requires.

Deploy autonomous agent crews with CrewAI to analyze European pension scenarios and find contribution gaps.

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Connect European Pension Estimator MCP to CrewAI

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Setup European Pension Estimator with CrewAI

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Assign Pension Analysis Roles

Build a specialized team. You can assign a 'Researcher' agent to use `get_scheme_details` for fetching the pension rules of a specific country. It then passes that information to the next agent. An 'Analyst' agent takes the data from the researcher and runs `calculate_monthly_benefit` to get the hard numbers. This division of labor is what makes CrewAI so effective.

Collaborative Gap Identification

Your agents can work together to find solutions. After the 'Analyst' gets a number, a 'Strategist' agent in the crew can use `assess_contribution_gap` to model different scenarios. Because the crew shares context, the 'Strategist' knows the user's goal and can test scenarios like working two extra years or making voluntary contributions. The final output is a complete plan, not just a single number.

Run Autonomous Pension Monitoring Crews

Set it and forget it. You can build a CrewAI team that runs on a schedule, for example, once a week. This autonomous crew can monitor a portfolio of clients using this MCP. If it detects a change in a pension scheme or a client's projected income drops, the crew can flag the account for review by a human advisor. This is hands-off financial monitoring.

Setup guide

Set up European Pension Estimator MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke European Pension Estimator tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="European Pension Estimator Analyst",
    goal="Access and analyze European Pension Estimator data via MCP.",
    backstory="Expert analyst with direct European Pension Estimator access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent European Pension Estimator transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="European Pension Estimator Analyst",
        goal="Access and analyze European Pension Estimator data via MCP.",
        backstory="Expert analyst with direct European Pension Estimator access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent European Pension Estimator transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 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 CrewAI

It's straightforward. Just include the MCP server's URL in the `mcps` list when you define an Agent in your crew. CrewAI handles the tool discovery and connection for you.

Yes, that's a perfect use case. You can create a sequential crew where one agent fetches rules, a second calculates the benefit, and a third analyzes the contribution gap. They pass the work from one to the next.

Yes. For more control, you can use `MCPServerHTTP` from `crewai.mcp` and provide a `tool_filter`. This lets you expose only the specific tools that a particular agent's role requires.

It works with both. You can set up a simple sequential process or have a manager agent delegate pension calculations to subordinate agents in a hierarchical crew.

The data is processed transiently. Information like work history is used for the calculation in real-time and then immediately purged from memory. The MCP server keeps no records of your queries.

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