ExpensifyAI

Talk to your Splitwise. Get CRED-grade spending analytics.

ExpensifyAI is a Model Context Protocol (MCP) server for Splitwise that lets any LLM client (Claude, etc.) manage shared expenses and produce deterministic, premium spending analytics — category breakdowns, monthly trends, per-member comparisons, and minimum-transaction settlement plans — rendered as a self-contained, offline HTML dashboard.

Built on top of the excellent tarunn2799/splitwise-mcp; extended with a deterministic analytics engine and a category-first dashboard.

Interactive dashboard, generated from synthetic data. Open examples/demo-dashboard.html in a browser to try it live — pick a date range and every section recomputes instantly.

Analytics (what makes this ExpensifyAI)

• Deterministic by construction — every number is computed in pure Python with Decimal math (no float drift, no LLM estimation). Same input → byte-identical output. Each report carries a reconciliation check: per-expense shares must sum to cost, or the mismatch is flagged.

• Category-first, à la CRED — an expandable "where it goes" view leads every report; tap a category to drill into its transactions.

• Seven analytics modules — category breakdown · monthly trend · owed-vs-paid ("mine vs split") · per-member comparison + category×member matrix · transaction ledger · top transactions · settlement optimizer (minimum transactions to settle a group — no other Splitwise tool has this).

• Interactive dashboard — CRED-grade dark UI with a live date-range picker + presets (this month / 3mo / 6mo / this year / all) that re-filter and recompute every section in the browser. Hand-rolled inline-SVG charts, validated colorblind-safe palette, fully offline (self-contained single file — no CDN, no server). All client math is integer paise, so the live recompute stays exact and reconciles against the Python source of truth.

• Two analytics tools — analyze_spending(target_type, target_id?, dates?, generate_dashboard?) and compare_group_members(group_id, …). target_type is me | group | friend.

Related MCP server: Splitwise MCP Server

Itemization, receipt scanning & default splits

Splitwise-Pro-parity, built deterministically:

• Structured itemization — create_itemized_expense(description, group_id, items, …) turns line-items into ONE expense where each item can split differently (beers ¾ to one person, groceries 4-way, cake between two). Each person's total owed_share is computed in exact integer paise (largest-remainder rounding, so an indivisible ₹100/3 still sums back to ₹100), and the expense is reconciled to its total before anything is written — a mismatch refuses to create rather than posting a wrong split. dry_run=True previews the computed split.

• Receipt scanning (LLM-vision-native) — no OCR engine, no cloud keys, no new dependencies: the calling agent (Claude) reads the receipt image, extracts line-items, and calls create_itemized_expense. The server owns the exact math and the Splitwise write.

• Receipt image upload — attach_receipt(expense_id, image_path) uploads a local image/PDF to an existing expense (multipart), so the receipt shows on it in the Splitwise app. Pairs with the scan flow: extract line-items from the image, then attach the image itself.

• Save default splits — save_default_split(name, split) / list_default_splits / delete_default_split. Reuse a template by putting "split_ref": "roomies-4way" on an item. Stored locally in ~/.expensifyai/splits.json.

Pick any date range — the whole dashboard recomputes live in the browser:

Try it without an account:

python examples/generate_demo.py   # writes examples/demo-dashboard.html

Features (MCP)

• Full API Access: Manage expenses, groups, friends, and comments.

• Natural Language Resolution: Fuzzy matching for names ("John" -> "John Smith") and groups.

• Dual Auth: Supports both OAuth 2.0 (recommended) and API Keys.

• Smart Caching: Optimizes performance for static data like categories and currencies.

Installation

git clone https://github.com/udaysrinu/ExpensifyAI
cd ExpensifyAI
python -m venv venv
source venv/bin/activate
pip install -e .

Configuration

See SETUP.md for detailed authentication and configuration instructions.

Quick Config

Run the included setup script:

python -m splitwise_mcp_server.oauth_setup

Use the keys provided there, and add all three to your mcp.json:

{
  "mcpServers": {
    "splitwise": {
      "command": "python",
      "args": ["-m", "splitwise_mcp_server"],
      "env": {
        "SPLITWISE_OAUTH_ACCESS_TOKEN": "your_token_here"
      }
    }
  }
}

Get your Auth Keys You can get your Consumer Key and Secret by registering an app at https://secure.splitwise.com/apps.

IMPORTANT: Using a Virtual Environment? If you installed the package in a venv or Conda environment, you must use the absolute path to the python executable in your config.

"command": "/absolute/path/to/venv/bin/python"

See SETUP.md for details.

Usage

The server enables natural language interactions with your Splitwise data.

Examples:

• "What's my current balance?"

• "Split a $50 dinner with Sarah."

• "Use the receipt I uploaded to split the dinner between Manav and me."

• "Show me expenses from last month."

• "Create a group called 'Ski Trip' with Mike."

Tools

See TOOLS.md for detailed documentation.

User Tools

• get-current-user: Get authenticated user information

• get-user: Get information about a specific user

Expense Tools

• create-expense: Create a new expense with splits

• get-expenses: List expenses with optional filters

• get-expense: Get detailed expense information

• update-expense: Update an existing expense

• delete-expense: Delete an expense

Group Tools

• get-groups: List all groups

• get-group: Get detailed group information

• create-group: Create a new group

• delete-group: Delete a group

• add-user-to-group: Add a user to a group

• remove-user-from-group: Remove a user from a group

Friend Tools

• get-friends: List all friends

• get-friend: Get detailed friend information

Resolution Tools

• resolve-friend: Fuzzy match friend names to user IDs

• resolve-group: Fuzzy match group names to group IDs

• resolve-category: Fuzzy match category names to category IDs

Comment Tools

• create-comment: Add a comment to an expense

• get-comments: Get all comments for an expense

• delete-comment: Delete a comment

Utility Tools

• get-categories: Get all expense categories

• get-currencies: Get all supported currencies

Arithmetic Tools

• add: Add multiple numbers

• subtract: Subtract numbers

• multiply: Multiply numbers

• divide: Divide numbers

• modulo: Calculate remainder

Development

# Setup
git clone https://github.com/udaysrinu/ExpensifyAI
cd ExpensifyAI
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

# Test
pytest                              # full suite
pytest tests/test_analytics.py      # deterministic analytics (15 tests)

# See the dashboard with no account
python examples/generate_demo.py    # writes examples/demo-dashboard.html

License

MIT License. See LICENSE for details.

Built on top of tarunn2799/splitwise-mcp (MIT); the analytics engine, interactive dashboard, and tests are added by ExpensifyAI.