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:
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.