lunchmoney-mcp
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- FlicenseNot gradedqualityDmaintenanceAn MCP server that enables AI assistants to interact directly with Lunch Money's financial API, allowing users to query transactions, access budget information, and perform financial analysis through natural language.-
- AlicenseAqualityAmaintenanceAn MCP server implementation that provides programmatic access to personal finance data through LunchMoney's API, enabling AI assistants to manage transactions, budgets, categories, and assets.5910,833 npm107MIT
- AlicenseNot gradedqualityCmaintenanceA Model Context Protocol server that allows AI assistants to interact with Lunch Money accounts, enabling management of transactions, categories, budgets, and other financial data through natural language commands.MIT
- AlicenseNot gradedqualityCmaintenanceA comprehensive MCP server that enables AI assistants to manage Lunch Money finances through 37 tools for transactions, budgets, and accounts. It supports both local stdio and remote HTTP transport modes with secure, encrypted credential storage.8 npm3MIT
- AlicenseBqualityCmaintenanceAn MCP server providing full integration with the Lunch Money API to manage financial data including transactions, budgets, assets, and categories. It enables AI assistants to perform CRUD operations on financial records through a standardized HTTP interface.26MIT
- AlicenseNot gradedqualityCmaintenanceAn MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.1MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: get-budget-summary targets budget overviews, get-category-spending focuses on category-level data, get-recent-transactions retrieves chronological transaction lists, and search-transactions enables keyword-based queries. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb-noun pattern with hyphens (e.g., get-budget-summary, search-transactions). The naming is uniform across all four tools, using 'get' for retrieval operations and 'search' for querying, which enhances predictability and readability.
With 4 tools, the count is reasonable for a personal finance domain, covering key operations like summaries, category analysis, and transaction retrieval. However, it feels slightly thin as it lacks tools for creating or updating data (e.g., adding transactions or categories), which might be expected in a full-featured finance server.
The tool set covers read-only operations well, including summaries, category spending, and transaction searches, but there are notable gaps. It lacks CRUD capabilities for transactions, categories, or budgets (e.g., create, update, delete), which limits agents to viewing data without modifying it, potentially causing workflow dead ends.