Overview
This lecture introduces the Model Context Protocol (mCP), an open standard for enabling AI applications and agents to interact seamlessly with external tools, resources, and data, aiming to make AI systems more context-rich and extensible.
Motivation & Philosophy
- mCP was created to standardize how AI models receive and use contextual information from external systems.
- Modern AI apps require integration with diverse data sources, unlike early chatbots, which relied on manual context entry.
- Fragmented, custom implementations for context integration prompted the need for an open protocol.
Core Concepts of mCP
- mCP provides three primary interfaces: tools (model-controlled actions), resources (application-controlled data), and prompts (user-controlled templates).
- Tools enable models to read, write, and update data or take actions across various systems.
- Resources are pieces of data (e.g., files, images, JSON) exposed to clients, managed by the application.
- Prompts are reusable, user-invoked templates for common tasks or workflows.
- mCP enables clear separation of control between model, application, and user.
Architecture & Adoption
- mCP defines a client-server architecture where any mCP client can connect to any mCP server, reducing integration complexity.
- Widespread adoption includes community-built and official servers, integration into IDEs, agent frameworks, and enterprise environments.
- Composability allows applications to function both as mCP clients and servers, supporting multi-agent, hierarchical, and modular systems.
Agent Framework Integration
- mCP complements agent frameworks by standardizing tool and data access, while agent frameworks manage the agent’s reasoning loop and memory.
- Agents can dynamically discover and utilize new tools and resources via mCP, enhancing self-evolution and flexibility.
Protocol Capabilities & Roadmap
- Sampling: Servers can request model completions from clients, maintaining privacy and flexibility.
- Remote servers supported via secure protocols and OAuth authentication.
- mCP registry under development for unified discovery, versioning, and verification of servers and tools.
- Planned support includes server-initiated actions, streaming, improved namespacing, and enhanced state management.
Key Terms & Definitions
- mCP (Model Context Protocol) — Open standard for AI applications to connect with external systems/tools.
- Tool — Functionality exposed by a server for the model to invoke (read/write/update actions).
- Resource — Data made accessible from the server to the client, managed by the application.
- Prompt — User-invoked, predefined template to structure model interactions.
- Sampling — Server-side request to the client for an LLM inference/completion.
- Composability — Ability for clients/servers to nest or chain, enabling modular agent architectures.
- Registry — Central catalogue for discovering, verifying, and managing mCP servers.
Action Items / Next Steps
- Review the mCP documentation for building servers and understanding protocol interfaces.
- Explore the mCP inspector tool for debugging and monitoring server interactions.
- Monitor the upcoming mCP registry for discovering and publishing servers.
- Consider the emerging best practices for security, versioning, and permission boundaries when deploying mCP solutions.