How to Use the Splitwise MCP in CrewAI

Build autonomous financial operations with crewai for Splitwise.

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Connect Splitwise MCP to CrewAI

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

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Setup Splitwise with CrewAI

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Claude

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Autonomous expense reconciliation.

Assign one agent to fetch all group members using `get_friends`. A second agent reviews the list of expenses from `get_expenses` and calculates discrepancies. This automated review saves hours of manual checking.

Multi-stage debt assessment.

You can deploy a team where Agent A gets group details (`get_group`), Agent B checks individual balances via `get_user`, and Agent C compiles the final report. This hierarchical execution handles complex analysis.

Automated user lookup and reporting.

The crew can run a specialized sequence: first, list all groups with `get_groups`. Then, for each group, it calls `get_expenses` to pull relevant data. This structured approach ensures no shared pot is missed.

Setup guide

Set up Splitwise 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 Splitwise tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Splitwise 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 →

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="Splitwise Analyst",
        goal="Access and analyze Splitwise data via MCP.",
        backstory="Expert analyst with direct Splitwise access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Splitwise 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 Splitwise. 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 Splitwise MCP in CrewAI

The framework lets you build multiple specialized agents that collaborate on money tracking. One agent might fetch data with `get_expenses`, while another agent analyzes the results to identify owed amounts.

Yes. You can define roles—like 'Auditor Agent' and 'Reminders Agent'—to handle everything from listing friends (`get_friends`) to flagging overdue payments.

The system handles user IDs, friend lists, and expense details. When the agents run, they access private financial information, which must be handled securely within your multi-agent operations.

You can build a process where one agent finds outdated expenses using `get_expenses`, and another agent confirms the deletion via `delete_expense`. This keeps your records accurate over time.

The crew can monitor additions and removals by listing all groups (`get_groups`). It processes the list to ensure that newly formed or dissolved groups are accounted for in the final balance report.

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