Lecture on mCP (Modular Contextual Protocol) and Startup Opportunities

Introduction to mCP

• Recent Popularity: mCP has gained significant attention recently.
• Basic Concept: Provides a unified way for AI agents or applications to access external assistance.
  • Helps improve AI coding workflows.
  • Differs from OpenAI’s function calling (T-call) by offering a standard format.

Comparison with Other Protocols

• Current Issues:
  • Different large language model providers (OpenAI, Claw, etc.) have varied formats for external interactions.
• mCP as a Solution:
  • Acts similar to TCP/IP in early internet days.
  • Offers a standardized format for communication, simplifying integration.

Startup Opportunities in the mCP Ecosystem

• Potential for New AI Agent Clients:
  • Lower entry barriers due to unified protocol.
  • AI agents like Cursor, Wing, etc., become easier to develop and integrate.
• Marketplace Creation:
  • Similar to app stores, marketplaces for mCPs can emerge.
  • Examples include existing websites that curate and test mCPs like GL and Smith.

Building an mCP Server

• Importance: Learning to build an mCP can enhance personal use or be the basis for a business.
• Simple Setup:
  • Example: Building a Figma mCP server using Python SDK.
  • Steps include installing mCP, defining functions, and setting up server commands.

Go-To-Market Strategy

• Essential for Success: Product creation is only half the battle; marketing is crucial.
• Playbook Recommendation: A free Go-To-Market playbook is recommended for launching new product types.

Detailed Example: Building Figma mCP

• Process Overview:
  • Utilize Figma API to extract data and convert designs into web pages.
  • Endpoint Usage:
    • Get file and node data from Figma.
  • Code Example:
    • Python script for creating and testing mCP functionalities.
    • Cleaning JSON responses for clarity and usability.

Distribution of mCP Servers

• Platforms for Distribution:
  • Existing platforms like GL and Myster for sharing mCP servers.
  • Important to document dependencies and provide user instructions.

Future Directions and Community Engagement

• Continuous Improvement: Ongoing work on Figma mCP to add features like reusable components and workflow extraction.
• Community Involvement:
  • Encouragement to join AI Builder Club Community for support and sharing.
  • Plea for feedback and suggestions on mCP development.

Conclusion

• Call to Action: Engage with the community, explore mCP development, and share insights.
• Video Resources: Links provided for further exploration and community joining.