Speaker: Mahes from the Applied AI team at Anthropic.
Topic: mCP (Model Context Protocol).
Format: Interactive talk with Q&A.
Overview of mCP
mCP is a protocol designed to improve AI applications by standardizing how they interact with external systems.
Motivation: Enhance model performance by providing rich, personalized context.
Context evolution: From manual input to seamless data integration.
Inspiration: APIs and LSP (Language Server Protocol).
mCP Structure: Interfaces for prompts, tools, and resources.
Pre-mCP Landscape
Fragmentation in AI system development.
Custom implementations within and across companies leading to inefficiencies.
Benefits of mCP
For Developers: Easier integration across AI apps with a standardized protocol.
For Enterprises: Centralized resource management and faster development.
For End Users: Enhanced AI capabilities with richer context.
Main Components of mCP
Tools: Model-controlled actions that can fetch, write, or manipulate data.
Resources: Data resources controlled by the application; can be static or dynamic.
Prompts: User-controlled interaction templates.
Adoption and Use Cases
Current Use: Applications like IDEs and servers; notable examples include Cloudflare and Stripe.
Community Engagement: Over 1100 community-built servers.
Examples in use: Cloud for desktop connecting with GitHub and Asana servers for integrated task management.
mCP and Agents
Agent Systems: Running loops involving tools, retrieval systems, and memory for intelligent task execution.
mCP as a Foundation: Facilitates seamless access to tools and resources for agent systems.
Building with mCP
Client and Server Roles: Logical, not physical separations; a system can be both.
Composability: Allows layering of AI systems through client-server chains.
Sampling: Servers can request completions from clients, enhancing autonomy and task flexibility.
Roadmap and Future Developments
Remote Servers and OAuth: Supporting remote server interactions and secure authentication.
Registry Development: A centralized place to discover and verify mCP servers.
Well-Known URLs: For top-down discovery of mCP capabilities, enhancing public server utility.
Advanced Protocol Features: Considering stateful vs stateless connections, streaming, and proactive server behavior.
Conclusion
mCP aims to revolutionize AI applications by improving how they integrate with and utilize external systems, paving the way for more efficient and effective AI-driven solutions.