Claude's MCP - give LLMs a second brain

Summary

In this video, the speaker demonstrates how to extend an AI model’s capabilities by adding custom functions, specifically through the Model Context Protocol (MCP). Using Python, the speaker shows how to add a counting function to Claude, a language model, to improve its accuracy. The discussion expands to the development of tools like Manus, which allows AI to autonomously plan tasks and generate code. The video also explores the potential of giving AI a personal memory system, organized like a Zettelkasten, to enhance its utility in managing knowledge and providing insightful responses over time.

Takeaways

• 😀 Claude can be enhanced by adding external Python functions through the Model Context Protocol (mCP).
• 😀 mCP allows Claude to access custom functions, helping it tackle tasks it may not handle well natively.
• 😀 Example: A Python function was created to count character occurrences in a string, enhancing Claude's abilities.
• 😀 Adding functions through mCP can improve Claude's accuracy in tasks that require custom logic, like counting specific characters in a string.
• 😀 The mCP server setup is straightforward and allows users to extend Claude’s functionality with their own code.
• 😀 While tools like Manus automate code writing, manually adding mCP servers can be more efficient for repeated tasks.
• 😀 One application of mCP is creating a memory system where Claude can retain and organize knowledge from past interactions.
• 😀 A 'Zle Caston' framework could allow Claude to maintain and organize its own knowledge, similar to a digital brain.
• 😀 This memory system could help Claude provide more personalized and context-aware responses in ongoing conversations.
• 😀 The ability for Claude to remember previous chats and link them to new information is a powerful way to enhance AI functionality, especially for research and learning tasks.

Q & A

What is the Model Context Protocol (MCP) and how does it enhance Claude's capabilities?

The Model Context Protocol (MCP) allows you to extend the functionality of an AI model like Claude by adding custom functions. By using the MCP, you can expose specific Python functions or scripts to Claude, enabling it to handle tasks it might not natively be able to do. In the example given, a function to count occurrences of a character in a string was added to Claude's abilities.

Why did the user need to write their own Python function for counting characters in the string?

Claude initially struggled to correctly count the number of occurrences of a character in a string. The user's Python code provided a more accurate and reliable solution to perform the task. By creating the custom function `count_chars`, the user was able to ensure Claude could count characters correctly when needed.

How does the MCP server allow Claude to execute external code?

The MCP server exposes custom functions, like the `count_chars` function, to Claude. Once this function is integrated into Claude’s configuration, the AI can invoke it during tasks, allowing Claude to use the external code to compute results. This integration happens by modifying the configuration file and restarting Claude.

What is the potential advantage of using MCP servers instead of relying on AI-generated code for repeated tasks?

Writing your own MCP server for repeated tasks can save time and improve efficiency. While tools like Manus can generate code for you, it might be slower and prone to errors. If the task is going to be repeated frequently, creating a custom MCP server ensures better performance and avoids the potential inefficiency of waiting for AI to generate the code each time.

What is the purpose of Manis in the context of MCP servers, and how does it relate to Claude?

Manis is a tool that leverages the MCP functionality to allow the AI to plan and write its own code for a task. Instead of relying on external pre-written code, Manis can generate the necessary MCP server for any given task. This allows for a more autonomous way of running tasks but might be less efficient if used for repetitive tasks.

What is the concept of a 'second brain' for AI, and how does it relate to the Zettelcasten framework?

The 'second brain' concept involves giving an AI its own organizational system for knowledge, similar to how humans use external tools like Zettelcasten to manage and connect their thoughts. By using the Zettelcasten framework, Claude could maintain its own knowledge base, linking new information to existing knowledge, thereby improving its ability to recall and reason through past interactions.

How could an LLM like Claude benefit from managing its own Zettelcasten?

By managing its own Zettelcasten, Claude could build a more effective knowledge system, retaining and connecting information across conversations. This would allow Claude to remember past interactions, improve its responses, and ensure a more coherent and personalized experience. It would also be capable of relating new information to what it has already learned.

What role does the Zettelcasten system play in AI-assisted research?

The Zettelcasten system aids AI-assisted research by providing a structured way to organize knowledge. When an AI uses this system, it can ingest, relate, and recall research papers or articles more effectively. Rather than just answering questions based on provided information, it could link new concepts with previously learned material, fostering deeper insights and more complex reasoning.

How does the Zettelcasten system differ from tools like Google's Notebook LM?

Tools like Google's Notebook LM allow you to input a document and query the AI about its contents. However, the AI doesn’t truly 'understand' or integrate the information beyond providing surface-level answers. In contrast, a Zettelcasten system allows the AI to actively organize and link new knowledge, enabling it to engage in more sophisticated reasoning and better understand the material it processes.

Why is it important for AI to have a memory system in the context of personal assistants?

A memory system allows an AI personal assistant to recall past conversations and interactions, providing more context-aware and coherent responses. This helps to make the assistant more useful and intuitive, as it can track progress, maintain context across different conversations, and personalize interactions over time, similar to how humans remember previous discussions.