How to Use the Splitwise MCP in Mastra AI

Q: What data does Splitwise touch regarding privacy with mastra-ai?

The server manages user IDs, group membership lists (getgroups), and financial transactions. The workflow engine treats all this information as mission-critical account details that require careful handling.

Q: Can I manage multiple Splitwise groups with mastra-ai?

You can create a dedicated workflow for each group. Using getgroups at the start of your process allows the agent to cycle through every shared pot and check its status.

Build resilient financial workflows with mastra-ai for Splitwise.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Splitwise MCP to Mastra AI

Create your Vinkius account to connect Splitwise to Mastra AI — 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 Mastra AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Automated debt collection flows.

Set up a workflow that checks outstanding balances using `get_expenses`. If the balance is negative, the agent can automatically trigger a reminder or send a payment request. This handles routine follow-ups without constant manual input.

Multi-step expense validation.

Need to process an expense but need three steps? The agent first gets group details via `get_group`, then verifies the user exists with `get_user`. Only if both succeed does it finally call `create_expense`.

Recovery for missing records.

If a payment is reported but no record exists, the agent runs a sequence: it calls `get_expenses` to check history. If nothing shows up, it flags the need for manual review via the workflow engine.

Setup guide

Set up Splitwise MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Splitwise tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "splitwise-mcp-client",
  servers: {
    "splitwise-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Splitwise Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Splitwise tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Splitwise transactions"
);
console.log(result.text);

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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 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.

Why Choose Vinkius

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Common questions about Splitwise MCP in Mastra AI

You can build complex workflows that manage money disputes. For instance, if a payment fails, the agent automatically retries using a different configured method—all without human intervention.

Yes. The workflow engine handles sequential logic perfectly. You can set up processes that iterate through many friends, calling `get_friends` and then checking their balances one by one.

The server manages user IDs, group membership lists (`get_groups`), and financial transactions. The workflow engine treats all this information as mission-critical account details that require careful handling.

You can create a dedicated workflow for each group. Using `get_groups` at the start of your process allows the agent to cycle through every shared pot and check its status.

While there isn't a direct 'update' tool, you can build a workflow that calls `delete_expense` followed immediately by `create_expense`. This simulates an edit and ensures data integrity.

Use it with your favorite AI tools

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