Vibe Coder MCP Server - v4 Final Release

> IMPORTANT NOTICE: This is the final v4 release of the Vibe Coder MCP Server, which includes the Automatic Contextual Retrieval System (ACRS) tools. Development has moved to v5 in a separate repository. This version is being made available to the community as a stable, feature-complete release.

New in v4: Automatic Contextual Retrieval System (ACRS)

The v4 release introduces the Automatic Contextual Retrieval System, which enhances AI assistant capabilities through:

- Contextual memory: Stores and retrieves relevant information based on the current context
- Advanced caching: Reduces redundant LLM calls and improves response times
- Semantic search: Finds related content based on meaning rather than exact text matching
- Sequential thinking: Breaks down complex problems into manageable steps

These tools enable more coherent, context-aware interactions with LLM-based assistants.

Getting Started with GitHub Version

Quick Installation

1. Clone the repository:

   git clone https://github.com/jsscarfo/vibe-coder-mcp-v4.git

   cd vibe-coder-mcp-v4

   

2. Setup:
- For Windows: setup.bat
- For macOS/Linux:

     chmod +x setup.sh

     ./setup.sh

     

3. Configure OpenRouter API Key:
- Create a .env file by copying .env.example
- Add your OpenRouter API key to the .env file

4. Integrate with your AI Assistant:
- Update your AI assistant's MCP configuration to include Vibe Coder
- See the full Setup Guide below for detailed instructions

ACRS Tools Usage

To use the Automatic Contextual Retrieval System tools:

1. Add memory entries:

   Add to memory: [content to remember]

   

2. Process requests with contextual enhancement:

   Process request [your request] with context

   

3. Enhance prompts for LLMs:

   Enhance prompt: [your prompt]

   

4. Get performance metrics:

   Get retrieval metrics

   

See the detailed documentation below for more information.