Memory Bank MCP
About
Memory Bank is an MCP server that helps teams create, manage, and access structured project documentation. It generates and maintains a set of interconnected Markdown documents that capture different aspects of project knowledge, from high-level goals to technical details and day
Details
- Author
- tuncer-byte
- GitHub stars
- 100
- Downloads
- 116
- Categories
- Knowledge Base
Jump to
- AI-generated documentation using the Gemini API
- Structured knowledge system with six core document types
- Implements the Model Context Protocol for LLM integration
- Customizable storage location for the Memory Bank directory
- Pre-defined templates for project brief, product context, and more
- Advanced querying with context-aware relevance ranking
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Memory Bank MCPCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install via git clone and npm install. Configure with a Gemini API key via a .env file or the mcp.json configuration. Run in development mode with npm run dev or in production mode after npm run build && npm run start. Add the server to your MCP client’s mcp.json to expose its tools for initialization, document updates, querying, and export.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"memory bank mcp": {
"memoryBank": {
"command": "node",
"args": [
"/path/to/memory-bank-mcp/dist/index.js"
],
"env": {
"GEMINI_API_KEY": ""
}
}
}
}
}McpServers
{
"memoryBank": {
"command": "node",
"args": [
"/path/to/memory-bank-mcp/dist/index.js"
],
"env": {
"GEMINI_API_KEY": ""
}
}
}Memory Bank MCP
<div align="center">
<h1>Memory Bank MCP</h1>
<p>
<b>Structured project knowledge management for LLMs via Model Context Protocol (MCP)</b>
</p>
</div>
<a href="https://glama.ai/mcp/servers/@tuncer-byte/memory-bank-MCP">
</a>
---
> Note: This is not a traditional Node.js application. Memory Bank MCP is an MCP server—a component in the Model Context Protocol ecosystem. It exposes project knowledge to LLM-powered agents and tools using a standardized protocol, enabling seamless integration with AI clients (e.g., Claude Desktop, IDEs, or custom LLM agents).
---
What is Model Context Protocol (MCP)?
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI: it provides a universal way to connect AI models to data sources and tools, both locally and remotely. MCP enables:
- Plug-and-play integrations between LLMs, data, and tools
- Switching between LLM providers with minimal friction
- Secure, modular architecture for building AI workflows
Learn more: MCP Introduction
About Memory Bank MCP
Memory Bank MCP is an MCP server that helps teams create, manage, and access structured project documentation. It generates and maintains interconnected Markdown documents capturing all aspects of project knowledge, from high-level goals to technical details and daily progress. It is designed to be accessed by MCP-compatible clients and LLM agents.
Features
- AI-Generated Documentation: Uses Gemini API to generate and update project documentation
- Structured Knowledge System: Maintains six core document types in a hierarchical structure
- MCP Server: Implements the Model Context Protocol for integration with LLM agents and tools
- Customizable Storage: Choose where your Memory Bank directory is created
- Document Templates: Pre-defined templates for project brief, product context, system patterns, etc.
- AI-Assisted Updates: Update documents manually or regenerate them with AI
- Advanced Querying: Search across all documents with context-aware relevance ranking
Installation
```bash
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