Bring Expense Tracking
to LlamaIndex
Learn how to connect Splitwise to LlamaIndex and start using 10 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the Splitwise MCP Server?
What you can do
- Track Expenses: Give your AI agent the ability to check who owes who across your distinct groups.
- Log Bills: Ask your AI to calculate receipts and accurately add the expense directly into Splitwise with automatic percentage or custom splits.
- Manage Friends & Groups: Easily pull details of group members.
How it works
1. Log into your Splitwise account. 2. Navigate to your Developer Applications settings to generate a personal API token. 3. Insert your Splitwise API Key directly into Vinkius vault.Who is this for?
Perfect for roommates, travel groups, and everyday individuals using AI tools like Claude/Cursor to parse complex grocery or travel receipts and directly sync them to Splitwise.Built-in capabilities (10)
Expects a stringified JSON object containing details like cost, description, format details (e.g. users__0__user_id=123, users__0__paid_share=5.00, etc. or JSON). Create an expense
Delete an expense
Get current user details
Get a specific expense
List expenses
Get friend details
List all friends
Get group details
List all groups
Get user details by ID
Why LlamaIndex?
LlamaIndex agents combine Splitwise tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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Data-first architecture: LlamaIndex agents combine Splitwise tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Splitwise tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Splitwise, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Splitwise tools were called, what data was returned, and how it influenced the final answer
Splitwise in LlamaIndex
Splitwise and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect Splitwise to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Splitwise in LlamaIndex
The Splitwise MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 10 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Splitwise for LlamaIndex
Every tool call from LlamaIndex to the Splitwise MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can the agent distribute the bill unequally?
Yes! When creating an expense, the AI agent can pass detailed users configuration in JSON to specify exact dollar amounts for each involved party.
How far back does the expense log go?
The agent typically limits the reading to the 20 most recent expenses to keep memory manageable, but can be instructed to pull earlier ones.
Can the bot settle debts?
Currently, the agent interacts with expenses. To log a debt settlement, the agent submits a specific 'payment = true' expense wrapper via the splitwise schema.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Splitwise tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp