AI Integration via MCP

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Overview

ReliaGrowR can expose its core analysis functions as Model Context Protocol (MCP) tools, allowing AI assistants such as Claude to call them directly during a conversation. This is powered by the mcptools package from Posit.

Once configured, an AI assistant can:

Fit reliability growth models on data you describe
Forecast future failures or MTBF
Run goodness-of-fit tests
Plan reliability demonstration tests
Analyze repairable systems with MCF and NHPP models

Installation

Install the required packages:

install.packages("mcptools")   # MCP server framework
install.packages("ellmer")     # Tool definition helpers (already in ReliaGrowR Suggests)

Starting the MCP Server

The server is started with a single call:

ReliaGrowR::rga_mcp_server()

By default this uses stdio transport (suitable for Claude Code and Claude Desktop). To use HTTP transport instead:

ReliaGrowR::rga_mcp_server(type = "http", port = 8080)

Configuring Claude Code

Add the server to Claude Code from your terminal:

claude mcp add -s user reliagrowR -- Rscript -e "ReliaGrowR::rga_mcp_server()"

The -s user flag stores the configuration in your user-level settings so it is available in every project.

Configuring Claude Desktop

Add the following block to claude_desktop_config.json (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "reliagrowR": {
      "command": "Rscript",
      "args": ["-e", "ReliaGrowR::rga_mcp_server()"]
    }
  }
}

Restart Claude Desktop after saving.

Available Tools

Tool	Function	Description
`rga`	`rga()`	Crow-AMSAA reliability growth model
`nhpp`	`nhpp()`	NHPP Power Law / Log-Linear for repairable systems
`duane`	`duane()`	Duane log-log regression
`mcf`	`mcf()`	Mean Cumulative Function (Nelson-Aalen)
`predict_rga`	`predict_rga()`	Forecast cumulative failures from RGA model
`predict_duane`	`predict_duane()`	Forecast MTBF from Duane model
`rdt`	`rdt()`	Reliability Demonstration Test planning
`gof_rga`	`gof()`	Goodness-of-fit statistics (CvM, K-S)

Example Session

With the MCP server running, you can ask Claude questions like:

“I have failure data with times [100, 200, 300, 400, 500] and failure counts [1, 2, 1, 3, 2]. Fit a Crow-AMSAA reliability growth model and forecast the cumulative failures at 1000 and 2000 hours.”

Claude will call rga and predict_rga on your behalf and return the results in plain language.

“Plan a reliability demonstration test for 90% reliability at 500 hours with 90% confidence, using a Weibull model with beta = 1.5 and 10 test units.”

Claude will call rdt and explain the required test duration.

Security Considerations

The MCP server runs R code in your local R session. Only share the server endpoint with trusted clients. For multi-user deployments, consider running the server in a sandboxed environment.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.