MCP: Model-Context-Protocol

In his AI Speaker Series presentation at Sutter Hill Ventures, David Soria Parra of Anthropic, shared insights on the Model-Context-Protocol (MCP), an open protocol designed to standardize how AI applications interact with external data sources and tools. Here's my notes from his talk:

Models are only as good as the context provided to them, making it crucial to ensure they have access to relevant information for specific tasks
MCP standardizes how AI applications interact with external systems, similar to how the Language Server Protocol (LSP) standardized development tools
MCP is not a protocol between models and external systems, but between AI applications that use LLMs and external systems
Without MCP, AI development is fragmented with every application building custom implementations, custom prompts, and custom tool calls
MCP separates the concerns of providing data access from building applications
This separation allows application developers to focus on building better applications while data providers can focus on exposing their data effectively

How MCP Works

Two major components exist in an MCP system: client (implemented by the application using the LLM) and server (serves context to the client)
MCP servers offer: Tools (functions that perform actions), Resources (raw data content exposed by the server), Prompts (show how tools should be invoked)
Application developers can connect their apps to any MCP server in the ecosystem
API developers can expose their data to multiple AI applications by implementing an MCP server once
Allows different organizations within large companies to build components independently that work together through the protocol


Writing Good Tools for MCP

Tools should be simple and focused on specific tasks
Comprehensive descriptions help models understand when and how to use the tools
Error messages should be in natural language to facilitate better interactions
The goal is to create tools that are intuitive for both models and users


Future Directions for MCP

Remote MCP servers with proper authorization mechanisms
An official MCP registry to discover available servers and tools
Asynchronous execution for long-running tasks
Streaming data capabilities from servers to clients
Namespacing to organize tools and resources
Improved elicitation techniques for better interactions
There's a need for a structure to manage the protocol as it grows