MyTruv: AI Money Autopilot

Apply a per-transaction override: change category, rename merchant, hide, or flag recurring / internal-transfer. Use the ``transaction_id`` returned by ``searchTransactions`` or ``transactionHistory``. Only the fields you set are changed; existing override values on this transaction are preserved. After applying, the response surfaces follow-ups the agent can offer the user: - ``similar_transactions``: other transactions sharing the same merchant in the last 90 days. If the user wants to apply the same change to those, call ``bulkRecategorizeTransactions`` with their ``transaction_id`` values. - ``rule_exists``: whether a standing rule already covers future transactions for this merchant. If ``false``, call ``createCategorizationRule`` to make the change stick going forward.

recategorizeTransaction

Get recurring transactions (subscriptions, bills, and income) from connected bank accounts. Identifies and returns recurring financial patterns including: - **Outflows**: Recurring expenses like subscriptions (Netflix, Spotify), bills (rent, utilities), memberships - **Inflows**: Recurring income like salary, freelance payments, dividends Each recurring transaction includes: - Source name and ID - Average amount and frequency - Status (active, inactive, irregular) - Category and account information - Historical transaction list - First detected and last transaction dates The response also includes an ``accounts`` list covering every account referenced by an inflow or outflow, so each item's ``account_id`` can be resolved to the account's name/mask/type against ``accounts[].id`` without a second lookup. **Parameters:** - `status` (optional): Filter by status - "active" (default), "inactive", "irregular", or "all" **Use cases:** - Find forgotten subscriptions to cancel - Track monthly bills and predict expenses - Verify recurring income sources - Budget planning based on fixed costs

recurringTransactions

Search and filter transactions across all connected bank accounts with flexible criteria. Supports text search, amount ranges, type filtering, and category filtering. Results are paginated — use offset to page through large result sets. **Parameters:** - `query` (optional): Text to search for in transaction descriptions and memos (case-insensitive substring match) - `min_amount` (optional): Minimum absolute transaction amount (e.g., 50.00) - `max_amount` (optional): Maximum absolute transaction amount (e.g., 500.00) - `transaction_type` (optional): Filter by type — "DEBIT" (money out) or "CREDIT" (money in) - `categories` (optional): Comma-separated category display names to filter (e.g., "Food & Dining,Groceries") - `days` (optional): Number of days to look back (default 90, max 365) - `sort_by` (optional): Sort field — "date" (default) or "amount" - `sort_order` (optional): Sort direction — "desc" (default, newest/largest first) or "asc" - `limit` (optional): Max results per page (default 20, max 50) - `offset` (optional): Number of results to skip for pagination (default 0) **Returns:** JSON object containing: - total_matches (int): Total number of matching transactions (before pagination) - transactions (list): Paginated list of matching transactions. Each transaction's ``date`` is the calendar day (YYYY-MM-DD) in the user's timezone; use it for any date you report so it matches what the user sees elsewhere in the app. - accounts (list): Accounts referenced by the returned transactions; match each transaction's ``account_id`` against ``accounts[].id`` to resolve account name/mask/type without a separate lookup. **Examples:** - Find coffee purchases: `query="coffee"` - Large debits over $500: `min_amount=500 transaction_type="DEBIT"` - Recent grocery spending: `categories="Groceries" days=30 sort_by="amount"` (use display names, e.g. "Food & Dining") - Page 2 of results: `query="amazon" offset=20 limit=20`

searchTransactions

Analyzes transaction spending patterns and returns detailed breakdown grouped by category, merchant, or time period. **Features:** - Group spending by category, merchant, or time period - Calculate trends comparing to previous period - Track spending over time with time series data - Filter by specific categories, accounts, or links - Double aggregation: group by time_period with a secondary breakdown by category or merchant **Parameters:** - `group_by` (optional): How to group the data - "category", "merchant", or "time_period". Defaults to "category". - `time_period` (optional): Time bucket size - "day", "week", "month", "quarter", or "year". Defaults to "month". - `link_id` (optional): Filter to specific link ID (defaults to all applicant's links) - `start_date` (optional): Start date in YYYY-MM-DD format (defaults to 3 periods back, minimum 90 days: day/week/month=90 days, quarter=270 days, year=1095 days) - `end_date` (optional): End date in YYYY-MM-DD format (defaults to today, cannot be in the future) - `account_ids` (optional): Comma-separated account IDs to filter (defaults to all user-selected accounts) - `categories` (optional): Comma-separated category display names to filter (e.g. "Food & Dining,Groceries") - `min_total_amount` (optional): Minimum spending amount to group separately (defaults to $50, lower amounts grouped as "Other") - `secondary_group_by` (optional): "category" or "merchant" — only valid when group_by=time_period. Returns a top-10 breakdown within each time period. **Examples:** - Group by category: `group_by=category time_period=month` - Filter by date range: `group_by=merchant time_period=week start_date=2024-01-01 end_date=2024-01-31` - Double aggregation: `group_by=time_period time_period=month secondary_group_by=category` **Note:** Transactions from `INVESTMENT`-type accounts (brokerage, crypto, IRA, etc.) are always excluded — stock purchases, short sales, dividend reinvestments, and margin interest are not personal spending. Passing an investment account ID in `account_ids` will therefore return empty results for that account. DEBIT transfers to investment platforms (Wealthfront, Robinhood, Fidelity, Schwab, Vanguard, Coinbase, etc.) are also excluded even when the investment account itself is not connected. The matching uses merchant name and description patterns, guarded against known false positives (e.g. "Les Schwab Tires", "Fidelity Life Insurance"). CREDITs from these platforms (withdrawals, dividends) are kept — they are real cashflow into the user's spending accounts.

spendingAnalysis

Retrieve all bank accounts and transactions from all connected bank accounts. This tool automatically fetches transactions from all connected bank accounts and combines them into a single report. Names in transaction descriptions are anonymized for privacy.

transactionHistory