Shiny is a proven way to build interactive data applications, but a Shiny app lives behind a URL. As AI assistants become a common place to explore and analyze data, that separation means users leave the conversation to use the app, then carry the results back by hand. In this edition of the R/Pharma Hangout sessions, James Wade (Dow) demonstrates [{shinymcp}](https://jameshwade.github.io/shinymcp/), which converts Shiny apps into MCP-compatible interfaces that render inside chat-based tools. Plots, inputs, and summaries appear inline, respond to user input, and execute code within the conversation ([background post](https://jameshwade.com/posts/2026-02-21_shinymcp.html)). The session also covers two related packages built on ellmer: {dsprrr}, which brings DSPy-style signatures, optimization, and tracing to R so prompts can be improved with data instead of hand-tuning, and {deputy}, an agent runtime for building tool-using AI agents with permissions, hooks, and multi-agent coordination. Together, these tools treat Shiny components as building blocks for agent-connected workflows rather than standalone dashboards.
James Wade
Dow
R/Pharma Hangout: Shinymcp
Shiny is a proven way to build interactive data applications, but a Shiny app lives behind a URL. As AI assistants become a common place to explore and analyze data, that separation means users leave the conversation to use the app, then carry the results back by hand. In this edition of the R/Pharma Hangout sessions, James Wade (Dow) demonstrates [ shinymcp ](https//jameshwade.github.io/shinymcp/), which converts Shiny apps into MCP-compatible interfaces that render inside chat-based tools. Plots, inputs, and summaries appear inline, respond to user input, and execute code within the conversation ([background post](https//jameshwade.com/posts/2026-02-21_shinymcp.html)). The session also covers two related packages built on ellmer  dsprrr , which brings DSPy-style signatures, optimization, and tracing to R so prompts can be improved with data instead of hand-tuning, and  deputy , an agent runtime for building tool-using AI agents with permissions, hooks, and multi-agent coordination. Together, these tools treat Shiny components as building blocks for agent-connected workflows rather than standalone dashboards.