Lunch Money MCP Server
An 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.
README Documentation
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.
🎯 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.
🤖 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
📁 Project Structure
├── docs/ # Project documentation and planning
├── rules/ # Agent execution rules and specifications
├── src/ # MCP server implementation
└── README.md # This file
🔧 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.