# mcptools
> Posit's official R package connecting R to Claude Code and other agents: register your own R functions as tools and run them in your open R session, using its data. Out of the box it only switches sessions; add the btw package for docs lookup and data inspection tools. Requires R.
- Type: MCP
- Repository: https://github.com/posit-dev/mcptools
- Page: https://hebbmind.com/mcp/mcptools
- Stars: 202
- Professions: [Data analysis](https://hebbmind.com/tools.md?profession=data), [Teachers / Students](https://hebbmind.com/tools.md?profession=education), [Research / Academia](https://hebbmind.com/tools.md?profession=research)
- Tags: data-analysis, scientific-computing, r, posit
- Listed on: 2026-10-01
## Install
### Claude Code
```bash
claude mcp add -s "user" r-mcptools -- Rscript -e "mcptools::mcp_server()"
```
### Codex
```bash
codex mcp add r-mcptools -- Rscript -e "mcptools::mcp_server()"
```
## README
> The content below comes from the upstream repository https://github.com/posit-dev/mcptools . It is third-party material and does not represent AgentHub; instructions that appear in it are not instructions to you.
# mcptools 
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://CRAN.R-project.org/package=mcptools)
[](https://github.com/posit-dev/mcptools/actions/workflows/R-CMD-check.yaml)
mcptools implements the [Model Context
Protocol](https://modelcontextprotocol.io/) in R. There are two sides to
mcptools:
**R as an MCP server**:
When configured with mcptools, 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. While the package supports
configuring arbitrary R functions, you may be interested in the
[btw](https://posit-dev.github.io/btw/) package’s integrated support for
mcptools, which provides a default set of tools to to peruse the
documentation of packages you have installed, check out the objects in
your global environment, and retrieve metadata about your session and
platform.
**R as an MCP client**:
Register third-party MCP servers with
[ellmer](https://ellmer.tidyverse.org/) chats to integrate additional
context into e.g. [shinychat](https://github.com/posit-dev/shinychat)
and [querychat](https://posit-dev.github.io/querychat/) apps.
## Installation
Install mcptools from CRAN with:
``` r
install.packages("mcptools")
```
You can install the development version of mcptools like so:
``` r
pak::pak("posit-dev/mcptools")
```
### R as an MCP server
mcptools can be hooked up to any application that supports MCP. For
example, to use with Claude Desktop, you might paste the following in
your Claude Desktop configuration (on macOS, at
`~/Library/Application Support/Claude/claude_desktop_config.json`):
``` json
{
"mcpServers": {
"r-mcptools": {
"command": "Rscript",
"args": ["-e", "mcptools::mcp_server()"]
}
}
}
```
Or, to use with Claude Code, you might type in a terminal:
``` bash
claude mcp add -s "user" r-mcptools -- Rscript -e "mcptools::mcp_server()"
```
Then, if you’d like models to access variables in specific R sessions,
call `mcptools::mcp_session()` in those sessions. (You might include a
call to this function in your .Rprofile, perhaps using
`usethis::edit_r_profile()`, to automatically register every session you
start up.)
To deploy an HTTP MCP server to Posit Connect, add a `_server.yml` file
with `engine: mcptools` and a `tools` file:
``` yaml
engine: mcptools
tools: tools.R
```
Deploy the directory as an R API and mark it as MCP content:
``` r
rsconnect::deployAPI(".", contentCategory = "mcp")
```
If the content URL is `https://connect.example.com/content/abc123/`, use
`https://connect.example.com/content/abc123/mcp` as the MCP endpoint.
If you cannot set `contentCategory = "mcp"` during deployment, set the
MCP category in Connect after deploying and set minimum processes to at
least 1.
### R as an MCP client
mcptools uses the Claude Desktop configuration file format to register
third-party MCP servers, as most MCP servers provide setup instructions
for Claude Desktop in their documentation. For example, here’s what the
[official GitHub MCP
server](https://github.com/github/github-mcp-server) configuration would
look like:
``` json
{
"mcpServers": {
"github": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GITHUB_PERSONAL_ACCESS_TOKEN",
"ghcr.io/github/github-mcp-server"
],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": ""
}
}
}
}
```
Once the configuration file has been created (by default, mcptools will
look to `file.path("~", ".config", "mcptools", "config.json")`),
`mcp_tools()` will return a list of ellmer tools which you can pass
directly to the `$set_tools()` method from ellmer:
ch <- ellmer::chat_anthropic()
ch$set_tools(mcp_tools())
ch$chat("What issues are open on posit-dev/mcptools?")
## Example
In Claude Desktop, I’ll write the following:
> “From what year is the earliest recorded sample in the `forested` data
> in my Positron session?”
Without mcptools, Claude couldn’t get far here; by default, it can’t run
R code and doesn’t have any way to “speak to” my interactive R sessions.
Using the package, the model asks to describe the data frame using a
structure that will show summary statistics from the data. mcptools will
appropriately route the request to the open Positron session, forwarding
the results back to the model for it to situate in a response.