Credit Karma MCP
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- AlicenseNot gradedqualityDmaintenanceConnects Claude Desktop to Yodlee financial data, enabling account management, transaction queries, spending summaries, and audio narrations.1MIT
- AlicenseNot gradedqualityDmaintenanceEnables Claude to analyze personal finances by reading bank CSVs or connecting to Plaid for live transactions, categorizing spending, flagging overspending against benchmarks, and generating actionable reports and savings tips.MIT
- AlicenseAqualityDmaintenanceEnables integration with Monarch Money to query financial data, analyze spending patterns, track budgets, and get personalized financial insights through conversational AI with Claude Desktop.113ISC
- FlicenseAqualityDmaintenanceEnables Claude to query Empower/Personal Capital financial accounts, net worth, transactions, and asset allocation via natural language.5-
- AlicenseNot gradedqualityBmaintenanceConnects Claude or ChatGPT to a user's own Google Sheet ledger so they can ask about their personal finances and log new spending in natural language. Exposes tools for monthly summaries, category breakdowns, top merchants, transaction listings, budget status, and expense creation.2,770 npmMIT
- AlicenseAqualityDmaintenanceIntegrates Claude with the BudgetBakers Wallet API to access financial data including accounts, transactions, categories, and spending analytics.810 npmMIT
TDQS
Scored across 11 tools
Most tools target distinct resources or aggregations (spending-by-category vs by-merchant vs account-summary vs balances are clearly separated). The one real overlap is ck_get_recent_transactions, which is explicitly a convenience shortcut for ck_list_transactions, and ck_query_sql is a generic escape hatch that can duplicate the analytics tools, but the descriptions make the intended boundaries clear.
Every tool follows the same ck_ prefix and snake_case verb_noun pattern (ck_list_transactions, ck_get_account_balances, ck_sync_transactions, ck_set_session). No mixed conventions or stray camelCase.
11 tools is well within the ideal range and each earns its place: data retrieval, aggregation, sync, session lifecycle, and diagnostics. Nothing feels padded or redundant enough to trim.
The surface covers the full read/analytics lifecycle: credential setup and teardown, sync, raw and filtered queries, categorical/merchant spending, account summaries and balances, plus a healthcheck for failure diagnosis. Minor gaps remain (no per-category or per-merchant detail drill-down, no budget/goal coverage), but core workflows have no dead ends.