The most frustrating problem when working with AI tools: having to start every session from scratch. Explain the project, provide context, describe what you're trying to do — and repeat it all over again next time.
memory-bank-MCP was born to solve this problem.
The Problem
When working on a long-running project with Claude Code, Cursor or a similar AI coding assistant, the assistant doesn't know the project's history, the decisions you've made, or the architecture. Every conversation starts from zero.
This isn't just inefficient — it can be dangerous. The assistant may make suggestions without knowing why a particular approach was chosen.
The Solution: Model Context Protocol
MCP (Model Context Protocol) is a standard developed by Anthropic for providing AI assistants with external tools and data sources.
memory-bank-MCP uses this protocol to:
- Store project context in structured Markdown files
- Automatically load that context at the start of every AI session
- Record important decisions, architecture choices, and progress
How It Works
// Load context when the server starts
const context = await readMemoryBank(projectPath);
// Provide context on every AI request
server.tool("get_project_context", async () => {
return { context: await loadAllMemories() };
});
// Save important decisions
server.tool("save_decision", async ({ decision, reasoning }) => {
await appendToMemory("decisions.md", { decision, reasoning, date: new Date() });
});
Results
The project reached 100+ GitHub stars and approximately 20,000 visitors. It became one of the most-used servers in the global MCP ecosystem.
The biggest lesson: solving a well-defined problem with a simple solution is far more effective than trying to do everything.
Explore or contribute: github.com/tuncer-byte/memory-bank-MCP
