Notes on Model Context Protocol (mCP) Overview

Introduction

• Model Context Protocol (mCP): Developed by Anthropic to standardize how tools are provided to large language models (LLMs).
• Significance: It's compared to USB ports for AI applications, offering a standardized way to connect tools to LLMs.
• Current Status: Unlike many hyped AI technologies, mCP is gaining steady traction and remains relevant.
• Advantage: Provides an unfair advantage for those who utilize it to enhance LLMs and AI agents.
• Availability: Exists since November last year and continues to grow in recognition and application.

mCP Functionality

• Analogy: mCP is like API endpoints for AI, enabling standardized exposure of tools to AI agents.
• Documentation: Available and comprehensive but with room for improvement, particularly in diagrams.
• Pre-mCP Challenges: Before mCP, tools for AI agents were less reusable across different frameworks.
• Post-mCP Solution: Standardizes and packages tools, allowing easier sharing and reuse.

Implementation and Use

• Diagrams: Illustrate the transition from bespoke tools to standardized mCP servers.
• Standardization: mCP servers act as a middleman for tool communication to AI agents, ensuring consistent usage.
• Misconception: mCP doesn’t change how tools are used but standardizes their accessibility.

mCP Clients and Servers

• Client Examples: Includes AI IDEs like Cursor, Wind Surf, and apps like Claw Desktop.
• Features: Focuses on tool standardization; other features like resource sharing are experimental.
• Server Examples: Official and community-driven servers available, e.g., Brave Search, Supabase, Redis.
• Setup: Servers can be set up using Docker, MPX, or Python, depending on their build.

Practical Demonstration

• Claw Desktop Setup: Demonstrated tool integration, showing how AI agents use mCP servers.
• Example Usage: Querying web search using Brave and Stagehand servers for web navigation.

Building with mCP

• Creating Custom Servers: Possible using AI coding assistance with Wind Surf or Cursor.
• n8n Integration: Utilizes community nodes for integrating mCP servers within n8n agents.
• Python Clients: Allows custom integration with frameworks like Pantic AI.

Future of mCP

• Potential: Though another protocol might arise, understanding mCP is beneficial due to its foundational concepts.
• Roadmap: Focuses on remote support, cloud integration, authentication, and agentic workflows.
• Vision: Anthropic’s roadmap hints at a robust future for mCP in AI tool standardization.

Conclusion

• Educational Value: Comprehensive understanding of mCP empowers developers to leverage AI more effectively.
• Call to Action: Encouragement for further exploration and development with mCP.