How to Use the Splitwise MCP in LlamaIndex

Q: Can I query old Splitwise transactions using LlamaIndex?

Yes, by indexing the outputs of getexpenses and getgroup, your agent can search through historical records and answer questions about past spending patterns.

Q: Does this MCP Server support creating new data entries in Splitwise and LlamaIndex?

Yes. The createexpense tool lets the agent write new expenses directly into Splitwise, which then updates the knowledge base for future queries.

Build RAG systems with LlamaIndex to query past Splitwise data using the MCP Server.

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Connect Splitwise MCP to LlamaIndex

Create your Vinkius account to connect Splitwise 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 Splitwise with LlamaIndex

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Search for specific transactions

Using `get_expense`, your agent pulls a single, detailed transaction record. This output becomes part of your knowledge base, allowing you to semantically search years later for that exact receipt amount. You can index the results from `get_expenses` to build a history log, letting you ask questions like, 'What did I pay for dinner last October?'

Understand friend relationships

`get_friends` lists all your contacts. Indexing these names lets the agent answer who is in common groups or who was involved in a specific expense without needing to manually query every group.

Manage group data via MCP Server

When you call `get_groups`, the output—a list of active shared accounts—is stored. This lets your RAG app answer questions like, 'Which groups are we currently tracking payments for?' The index allows combining this group metadata with expense details from `get_group` for richer answers.

Setup guide

Set up Splitwise 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 Splitwise 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 Splitwise tools.",
)
response = await agent.run("List recent Splitwise 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 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 LlamaIndex

You feed the results of multiple tool calls (e.g., listing expenses and groups) into your index. Your agent then queries this combined knowledge base to give you an answer about debt that's grounded in real API data.

Yes, by indexing the outputs of `get_expenses` and `get_group`, your agent can search through historical records and answer questions about past spending patterns.

The MCP Server exposes identity information (users, friends) and all financial transaction details. This allows you to create a comprehensive knowledge graph for your RAG application.

Yes. The `create_expense` tool lets the agent write new expenses directly into Splitwise, which then updates the knowledge base for future queries.

The server tracks financial records (costs, shares), group memberships, friend lists, and individual user identities. This is the core dataset for your knowledge index.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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