Model Context Protocol (MCP)

• Allows LLMS to more easily integrate with external tools
  • It establishes a standard interface for APIs to communicate to LLMs. This way all types of APIs and their individual patterns can be integrated into an LLM app without too much hassle.
  • Without MCP
    
    • You have build integration for each application and tool
  • With MCP
    
    • Interactions are standardized.
• Notes from MCP (Model Context Protocol): Simply explained in 5 minutes
• Packages
  • {mcptools} - Allows MCP-enabled tools like Claude Desktop, Claude Code, and VS Code GitHub Copilot can run R code in the sessions you have running to answer your questions
    • Works well with {btw}
  • {mcpr} - Enables R applications to expose capabilities (tools, resources, and prompts) to AI models through a standard JSON-RPC 2.0 interface. It also provides client functionality to connect to and interact with MCP servers
• Resources
  • Docs
  • Model Context Protocol servers - Links to official mcp servers and community-based servers
• Servers
  • R Econometrics MCP Server
• Protocol
  
  • MCP Client: Various clients and apps you use, like Cursor, know how to talk using the “MCP Protocol.”
  • MCP Servers: Providers like Sentry, Slack, JIRA, Gmail, etc. set up adapters around their APIs that follow the MCP Protocol.
    • They convert a message like “Get me the recent messages in the #alerts channel” to a request that can be sent to the Slack API
• Server File(s) Components
  • Defines a set of tools for the MCP Server to implement
  • Creates the server to listen for incoming requests
  • Defines a `switch` case that receives the tool call and calls the underlying external API, like the Slack API.
  • Wire up transport between Client and Server
    • Allows the MCP client and server to communicate more simply than standard HTTP requests. Instead, they can read from standard IO when communicating.