Brief Summary
The video discusses the creation and implementation of a custom Memory Bank MCP (Multi-Context Processor) server to improve context sharing between subtasks in AI development, particularly within the Roo Code environment. The creator expresses dissatisfaction with the original implementation of the Roo Code memory bank and demonstrates how to build a more efficient MCP server using Klein and the sequential thinking MCP as a base. The new MCP server is designed to ensure that all subtasks have access to the overall context of the main task, addressing a key limitation in current AI task orchestration. The creator also shares the MCP server on GitHub, providing a valuable tool for the AI development community.
- Creation of a custom Memory Bank MCP server for improved context sharing.
- Implementation using Klein and sequential thinking MCP as a base.
- Addresses the issue of subtasks lacking overall context in AI task orchestration.
- Sharing the MCP server on GitHub for community use.
Why Roo Code Memory Bank Needs Improvement
The creator expresses reservations about the existing Roo Code memory bank implementation, particularly its use of custom instructions. Dissatisfied with its implementation, the creator decided to develop a custom MCP server.
Creating My Own Memory Bank MCP Server
The creator explains the process of building a custom MCP server. Using the sequential thinking MCP server as a base, the creator integrates the functionalities of the Roo Code memory bank. This involves leveraging third-party APIs and providing specific GitHub links to Klein, instructing it to create an MCP server that accomplishes the desired tasks.
The Simple Prompt That Built The Entire MCP
The creator shares the prompt used to instruct Klein in building the MCP server. The prompt involves cloning both the sequential thinking MCP and the Roo Code memory bank projects into a new directory. The key instruction is to implement the Roo Code memory bank using the methodology of the sequential thinking MCP, effectively creating an MCP version of the memory bank.
How I Got Klein To Build The MCP In 5 Minutes
The creator details the speed and ease with which the MCP server was created using Klein. Within minutes, Klein generated the project with minimal adjustments required. The primary modification involved ensuring that the necessary settings were added to the Klein settings to ensure proper functionality.
The Context-Sharing Problem This MCP Solves
The creator explains the problem that the Memory Bank MCP solves, which is the lack of overall context in subtasks when breaking down a larger task. The Roo Code memory bank aims to fix this by ensuring each subtask has the necessary context. The creator's MCP server seeks to replicate this functionality with a more efficient implementation.
Testing The Memory Bank MCP With Roo Code
The creator tests the newly created Memory Bank MCP to ensure it functions correctly. A website-building prompt is used to test whether the MCP can split the task into subtasks and create the necessary files. The memory bank functions by writing down all prompts into a document and feeding them to the next prompt.
Setting Up The Memory Bank In Klein Settings
The creator integrates the Memory Bank MCP into Klein by adding it to the custom instructions for all modes. This ensures that the memory bank is accessible and functional within the Klein environment. The creator emphasizes the importance of keeping the custom instructions box dedicated to specific tasks, highlighting the value of the MCP server.
Seeing Memory Bank Working In Real-Time
The creator demonstrates the Memory Bank MCP working in real-time. The system creates a memory bank, which is helpful in passing information to each subtask. The MCP writes down the task details, ensuring that every subtask receives the necessary context.
Fixing The "Always Allow" Setting Issue
The creator addresses an issue with the settings, specifically the need to change "auto approve" to "always allow." This adjustment ensures that the MCP functions correctly and consistently.
How This MCP Passes Context To All Subtasks
The creator explains how the MCP passes context to all subtasks, ensuring that each subtask has the necessary information to complete its part of the overall task. By reading the context every time, the MCP solves the problem of subtasks lacking the necessary background information.
Preparing To Share The MCP Server On GitHub
The creator prepares to share the MCP server on GitHub, making it available to the broader AI development community. The MCP server is first shared with the creator's school community before being released as a video.
Using Klein To Push The Code To GitHub
The creator uses Klein to push the code to GitHub, simplifying the process of sharing the project. Klein's capabilities make it easier to manage GitHub tasks, which the creator appreciates due to a dislike for the traditional GitHub interface.
Adding A Readme File For Easy Installation
The creator adds a readme file to the GitHub repository, providing instructions on how to set up the MCP server, including how to configure the settings list.json file. This ensures that others can easily install and use the MCP server.
Final Test Of Memory Bank Working With Subtasks
The creator conducts a final test to ensure the Memory Bank MCP is working correctly with subtasks. The test confirms that the memory bank exists and can be read by the subtasks, demonstrating the effectiveness of the MCP in passing context.
The Holy Quintupelly Of AI Development
The creator expresses excitement about the successful implementation of the Memory Bank MCP, referring to it as the "Holy Quintupelly" of AI development. The ability to have memory and context passed between tasks is highlighted as a significant advancement.
Final Thoughts On Memory Bank MCP Value
The creator concludes by emphasizing the value of the Memory Bank MCP and expressing hope that others will recognize its benefits. The video encourages viewers to explore the GitHub repository and support the creator's school for further learning and development in AI.
