inst/doc/mcp_server.R

In autoslider.core: Slide Automation for Tables, Listings and Figures

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## -----------------------------------------------------------------------------
# install.packages("mcptools")   # MCP server runtime
# # ellmer and autoslider.core are already in your renv/library

## -----------------------------------------------------------------------------
# system.file("mcp/autoslider_mcp_server.R", package = "autoslider.core")

## -----------------------------------------------------------------------------
# system.file("mcp/autoslider_mcp_server.R", package = "autoslider.core")

## -----------------------------------------------------------------------------
# library(autoslider.core)
# library(dplyr)
# library(filters)
# 
# filters::load_filters(
#   system.file("filters.yml", package = "autoslider.core"),
#   overwrite = TRUE
# )
# 
# outputs <- read_spec(system.file("spec.yml", package = "autoslider.core")) |>
#   filter_spec(program %in% "t_dm_slide", verbose = FALSE) |>
#   generate_outputs(
#     datasets = list(
#       adsl = eg_adsl |> mutate(FASFL = SAFFL),
#       adae = eg_adae
#     ),
#     verbose_level = 0
#   ) |>
#   decorate_outputs()
# 
# prompt_list <- get_prompt_list(
#   system.file("prompt.yml", package = "autoslider.core")
# )
# 
# # Ollama / DeepSeek — no API key, runs fully offline
# outputs_ai <- get_ai_notes(
#   outputs     = outputs,
#   prompt_list = prompt_list,
#   platform    = "ollama",
#   model       = "deepseek-r1:1.5b",
#   base_url    = "http://localhost:11434"
# )
# 
# generate_slides(outputs_ai, outfile = "slides_local.pptx")