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
- {fastapi_mcp} - Exposes FastAPI endpoints as Model Context Protocol (MCP) tools with Auth (i.e. takes an existing FastAPI application and essentially “turning it into” an MCP server)
- {plumber2mcp} - Takes a plumber API and exposes its endpoints as MCP utilities (Same as {fastapi.mcp})
  - By adding MCP support to your Plumber API, you make your R functions available as:
    - Tools: AI assistants can call your API endpoints directly
    - Resources: AI assistants can read documentation, data, and analysis results
    - Prompts: AI assistants can use pre-defined templates to guide interactions
Resources
- Docs
- Model Context Protocol servers - Links to official mcp servers and community-based servers
Servers
- R Econometrics MCP Server
- mcp-repl - A MCP server that exposes a long-lived interactive REPL runtime over stdio.
  - It is backend-agnostic in design. The default interpreter is R, with an opt-in Python interpreter (–interpreter python).
  - Session state persists across calls, so agents can iterate in place, inspect intermediate values, debug, and read docs in-band.
Skills (not sure where to put this section yet)
- Packages
  - {wf} - Manage skills for large language model coding agents.
    - Supports installing skills from ‘GitHub’ or local directories, tracking versions in a lock file, and keeping installations current.
    - Installations can be scoped to a single project or shared globally across projects.
- Terms (source)
  - Claude Code (Website): A command-line interface (CLI) that brings Claude into your terminal.
  - CLAUDE.md (Docs): A configuration file where you can give Claude Code project-specific context, preferences, and instructions.
    - Sarah Johnson’s for developing using tidyverse
  - CLAUDE Skill (Docs): Reusable, specialized commands that help Claude handle common development tasks more consistently.
- Resources
- Skills
  - R
    - Posit Skills
      - Package development, Testing, Shiny, and Quarto brand_yml, Crafting Release Posts
    - Get latest posit news skill
    - Claude Code R Skills: A curated collection of Claude Code configurations for modern R use
      - Modular Skills (tidyverse, rlang, performance, OOP, testing)
      - Enforcement Rules (security, testing, git workflow)
      - Workflow Commands (planning, code review, TDD)
      - Context Management Hooks
    - r-performance
      - Teaches R performance optimization through profiling and benchmarking with profvis, bench::mark, vctrs, and parallel processing, including decision guidance on data.table vs dplyr trade-offs.
    - review-r
      - Comprehensive review of R scripts across code quality, reproducibility, domain correctness, and professional standards. Generates detailed reports without modifying source files
    - Packages
      - marginaleffects skill for both R and Python and both Claude and Codex
  - Tools/Platforms
    - Dagster Claude Skills
    - Hugging Face Skills - Definitions for AI/ML tasks like dataset creation, model training, and evaluation. They are interoperable with all major coding agent tools like OpenAI Codex, Anthropic’s Claude Code, Google DeepMind’s Gemini CLI, and Cursor.
Using MCP servers rather than the cloud service CLI tools (e.g. BigQuery CLI) provides better security control over what LLM products (e.g. Claude Code) can access, especially for handling sensitive data that requires logging or has potential privacy concerns.
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.
Example: Use {btw} to start a local MCP server for your R packages
```
claude mcp add -s "user" r-btw -- Rscript -e "btw::btw_mcp_server()"
```
- Now Claude Code can answer questions about ANY R package installed on your system.