Lunch-Money-MCP

KoltonG/Lunch-Money-MCP

3.2

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The Lunch Money MCP Server provides seamless access to financial data through a Model Context Protocol server, enabling AI assistants to interact with the Lunch Money API using standardized tools.

Lunch Money MCP Server

LLM agent-driven development of a Model Context Protocol server for Lunch Money API access through systematic agent execution with rigorous validation processes.


šŸŽÆ Repository Goal

This repository builds a Model Context Protocol (MCP) server that provides seamless access to Lunch Money financial data via standard IO (stdio) transport.

Goal 1: Enable AI assistants to interact directly with Lunch Money's API through standardized MCP tools using stdio (not remote), allowing users to:

  • Query transaction data with flexible filtering
  • Access spending categories and budget information
  • Retrieve transaction tags and organizational data
  • Perform financial analysis through natural language

🚧 Work in Progress

This project is actively under development using a systematic agent execution approach. Every line of code, configuration, and documentation is implemented through LLM agents following structured workflows.

šŸ¤– LLM Agent-Driven Development

This repository showcases a novel development methodology where:

  • LLM agents execute all coding tasks following predefined rules and validation checkpoints
  • No manual coding - agents handle implementation, testing, and documentation
  • Systematic validation ensures quality through mandatory human approval at each step
  • Structured task management breaks complex features into validated sub-tasks

Agent Execution Framework

Significant engineering effort has been invested in creating comprehensive rules and processes that enable:

  • Self-executing agents that can autonomously implement features
  • Clear validation marks with mandatory human approval between sub-tasks
  • Quality assurance through structured TDD and testing requirements
  • Systematic progression from PRD → TDD → Tasks → Implementation

The agent execution rules in /rules/ define:

  • Task breakdown and dependency management
  • Validation checkpoints and quality gates
  • Branch management and PR generation
  • Error handling and feedback loops

šŸ“ Project Structure

ā”œā”€ā”€ docs/                    # Project documentation and planning
ā”œā”€ā”€ rules/                   # Agent execution rules and specifications
ā”œā”€ā”€ src/                     # MCP server implementation
└── README.md               # This file

šŸ”§ Technology Stack

  • Runtime: Bun (fast TypeScript execution)
  • Framework: Model Context Protocol SDK
  • Validation: Zod schemas
  • HTTP Client: Axios
  • Testing: Built-in bun test runner

This README will be updated as the project progresses through agent-driven development milestones.