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# acquaint
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[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://CRAN.R-project.org/package=acquaint)
[](https://github.com/simonpcouch/acquaint/actions/workflows/R-CMD-check.yaml)
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The goal of acquaint is to enable LLM-enabled tools like Claude Code to
learn about the R packages you have installed using the [Model Context
Protocol](https://modelcontextprotocol.io/) (MCP). Tools configured with
acquaint can peruse package documentation and learn to use R packages even
if they weren’t included in their training data.
acquaint is written primarily in TypeScript but is distributed as an R
package for ease of distribution/install for R users. Installing the
package ensures you have the needed dependencies and provides a
shortcut, `mcp_config()`, to help you configure the tool with other
applications.
> IMPORTANT: This is an early proof of concept. Use at your own risk!
## Installation
You can install the development version of acquaint like so:
pak::pak("simonpcouch/acquaint")
acquaint can be hooked up to any application that supports MCP. Use
`mcp_config()` to generate the config for your machine for common
applications. For example, **Claude Code**:
library(acquaint)
mcp_config("Claude Code")
#> In a terminal, run:
#>
#> claude mcp add -s "user" r-acquaint node /Users/simoncouch/Library/R/arm64/4.4/library/acquaint/node/dist/index.js
Or, **Claude Desktop**:
mcp_config("Claude Desktop")
#> Run `file.edit(~/Library/Application Support/Claude/claude_desktop_config.json)`
#>
#> Then, paste the following:
#> {
#> "mcpServers": {
#> "r-acquaint": {
#> "command": "node",
#> "args": ["/Users/simoncouch/Library/R/arm64/4.4/library/acquaint/node/dist/index.js"]
#> }
#> }
#> }
## Example
In Claude Desktop, I’ll write the following:
> Using the R packages I have installed, write code to download data on
> flights in/out of Chicago airports in 2024.
In a typical chat interface, I’d be wary of two failure points here:
1. The model doesn’t know which packages I have installed.
2. If the model correctly guesses which packages I have installed,
there may not be enough information about how to *use* the packages
baked into its weights to write correct code.
<img src="https://github.com/user-attachments/assets/821ea3d6-4e30-46d6-ab9b-301276af2c35" alt="A screencast of a chat with Claude. I ask 'Using the R packages I have installed, write code to download data on flights in/out of Chicago airports in 2024.' and, after searching through the documentation of my currently installed R packages, Claude writes R code to do so." width="100%" />
Through first searching through my installed packages, Claude can locate
the anyflights package, which seems like a reasonable solution. The
model then discovers the package’s `anyflights()` function and reads its
documentation, and can pattern-match from there to write the correct
code.
