Finance Intelligence MCP
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- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to analyze personal financial data from Open Banking sources stored in PostgreSQL database. Provides educational financial analysis tools with intelligent formatting for learning about spending patterns, account balances, and transaction history.2ISC
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to access and manage personal financial data from US institutions and manual entries, including transactions, balances, liabilities, and investments.MIT
- FlicenseCqualityDmaintenanceEnables AI assistants to manage personal finances by storing, analyzing, and exporting expense data using a persistent PostgreSQL database. Supports adding/editing expenses, generating spending summaries, detecting top categories, and creating monthly reports.12-
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to access financial data from 20,000+ banks across 40+ countries, allowing users to query account balances, transactions, and spending patterns through natural language.4MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to access and analyze MonarchMoney personal finance data through natural language queries. Provides comprehensive financial insights including account balances, transaction analysis, budget tracking, and spending patterns with enterprise-grade security.10MIT
- AlicenseNot gradedqualityCmaintenanceTurns a personal-finance SQLite database into typed, schema-validated tools that an AI assistant can call directly, letting you manage accounts, transactions, budgets, debts, investments, tax estimates, and goals through natural language.41 npmMIT
TDQS
Scored across 12 tools
Each tool targets a distinct aspect of expense or budget management. The analytical tools (expense_breakdown, expense_summary, compare_budget_vs_expenses) have some conceptual overlap, but their descriptions clarify the differences (tabular grouping vs. chart-based summary vs. real-time comparison). The CRUD tools are clearly separated by resource (expenses vs. budgets).
Most tools follow a verb_noun pattern (list_expenses, add_expense, delete_budgets), but three tools are noun phrases (expense_breakdown, expense_summary, financial_health_score). There is also a minor inconsistency with 'add_expense' vs. 'create_budget', using different verbs for the same create action.
With 12 tools, the server is well-scoped for a financial management domain. Each tool serves a clear purpose, covering CRUD for expenses and budgets plus analytical insights, without excessive fragmentation or unnecessary overlap.
The tool surface provides full CRUD for both expenses and budgets, with list operations that support filters to act as read-by-query. Analytical coverage is strong with breakdown, summary, budget comparison, and health score, leaving no obvious dead ends for typical expense-tracking workflows.