MCP Tools

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MCP Tools

Overview

The Model Context Protocol (MCP) is an open standard that lets AI assistants call external tools as part of a conversation. Two MCP servers together cover the full ReliaPlotR analysis pipeline:

ReliaPlotR server — reliapltr_mcp_server() — life data and ALT analysis:

Tool	What it does
`fit_weibull`	Fit a Weibull or lognormal distribution to time-to-failure data and return parameter estimates
`fit_alt`	Fit an ALT model across multiple stress levels and return life-stress relationship coefficients
`plot_weibull`	Fit a distribution and return a Weibull probability plot as a plotly JSON string
`plot_alt`	Fit an ALT model and return probability and life-stress plots as plotly JSON strings
`plot_rga`	Fit a Crow-AMSAA model and return a cumulative failure plot as a plotly JSON string

ReliaGrowR server — rga_mcp_server() — reliability growth and repairable systems:

Tool	What it does
`rga`	Fit Crow-AMSAA reliability growth model; returns λ, β, and goodness-of-fit stats
`nhpp`	Fit a Power Law or Log-Linear NHPP process; returns model parameters
`duane`	Fit a Duane log-log growth model; returns slope, intercept, and GOF stats
`mcf`	Compute Nelson-Aalen MCF for repairable systems; returns time, MCF, and bounds
`predict_rga`	Forecast cumulative failures from a fitted Crow-AMSAA model
`predict_duane`	Forecast MTBF from a fitted Duane model
`rdt`	Reliability demonstration test planning — solve for required sample size or test time
`gof_rga`	Goodness-of-fit statistics (CvM, KS) for a Crow-AMSAA model

Installation

mcptools and ellmer are optional dependencies listed in Suggests. Install them before starting the server:

pak::pak(c("mcptools", "ellmer"))

Registering with Claude Code

Once the packages are installed, register both servers in your Claude Code session.

ReliaPlotR server (life data and ALT analysis):

# Option 1: inline (works with any R installation)
claude mcp add -s user reliapltr -- \
  Rscript -e "ReliaPlotR::reliapltr_mcp_server()"

# Option 2: bundled launcher script (after package installation)
claude mcp add -s user reliapltr -- \
  Rscript $(Rscript -e "cat(system.file('mcp/server.R', package='ReliaPlotR'))")

ReliaGrowR server (reliability growth and repairable systems):

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

Confirm both servers are registered:

claude mcp list

Once registered, Claude Code will offer all tools automatically whenever it detects a reliability analysis task.

The `fit_weibull` Tool

fit_weibull fits a Weibull or lognormal distribution to a vector of failure times, optionally with right-censored (suspension) data, and returns parameter estimates and a goodness-of-fit metric.

Arguments

Argument	Type	Default	Description
`failures`	numeric array	required	Failure times (positive numbers)
`suspensions`	numeric array	`NULL`	Right-censored times – units still operating at end of test
`dist`	string	`"weibull"`	Distribution: `"weibull"`, `"lognormal"`, or `"weibull3p"`
`method_fit`	string	`"mle"`	Estimation method: `"mle"` (recommended for censored data) or `"rr-xony"` (rank regression)
`method_conf`	string	`"lrb"`	Confidence bounds: `"lrb"` (likelihood-ratio, requires MLE) or `"fm"` (Fisher-matrix). Automatically switches to `"fm"` when rank regression is used.

Example

The following shows what the tool returns when called from an MCP client. The same function can be invoked directly in R for testing:

# Five machines that failed at these times (hours)
tool <- ReliaPlotR:::.make_wblr_tool()
result <- tool(failures = c(30, 49, 82, 90, 96))
str(result)
#> List of 12
#>  $ dist       : chr "weibull"
#>  $ method_fit : chr "mle"
#>  $ param1_name: chr "Beta"
#>  $ param1     : num 3.2
#>  $ param2_name: chr "Eta"
#>  $ param2     : num 77.8
#>  $ param3_name: chr NA
#>  $ param3     : num NA
#>  $ gof_metric : chr "loglikelihood"
#>  $ gof_value  : num -23.2
#>  $ method_conf: chr "lrb"
#>  $ n_failures : int 5

The flat named list is serialized to JSON when returned over MCP. param1 is the Weibull shape parameter $\beta$; param2 is the characteristic life $\eta$.

Adding right-censored suspensions shifts the estimates to account for units that did not fail:

result_susp <- tool(
  failures    = c(30, 49, 82, 90, 96),
  suspensions = c(100, 120, 150)
)
cat("Beta:", result_susp$param1, "\n")
#> Beta: 1.892207
cat("Eta: ", result_susp$param2, "\n")
#> Eta:  122.6721

The `fit_alt` Tool

fit_alt runs the full ALT pipeline (alt.make → alt.parallel → alt.fit) across multiple stress levels and returns per-stress-level parameters alongside the fitted global life-stress relationship.

Arguments

Argument	Type	Default	Description
`stresses`	numeric array	required	One stress value per stress level, e.g. `[300, 350, 400]`
`failures_json`	string	required	JSON array of failure-time arrays, one inner array per stress level. Example: `'[[248,456,528],[164,176,289],[88,112,152]]'`
`dist`	string	`"weibull"`	Life distribution: `"weibull"` or `"lognormal"`
`alt_model`	string	`"arrhenius"`	Life-stress model: `"arrhenius"` (temperature) or `"power"` (non-thermal)

Example

tool_alt <- ReliaPlotR:::.make_alt_tool()
result_alt <- tool_alt(
  stresses      = c(300, 350, 400),
  failures_json = "[[248,456,528,731,813,537],[164,176,289],[88,112,152]]",
  dist          = "weibull",
  alt_model     = "arrhenius"
)

# Per-stress-level parameter estimates
result_alt$parallel
#>   stress       P1       P2 wt n_failures
#> 1    300 615.9660 3.935945  6          6
#> 2    350 231.8588 3.935945  3          3
#> 3    400 128.0115 3.935945  3          3

# Global Arrhenius life-stress relationship coefficients
result_alt$relationship
#> $model
#> [1] "arrhenius"
#> 
#> $coef1
#> [1] 11.20605
#> 
#> $coef2
#> [1] -0.01605895

The P1 column in $parallel is the characteristic life $\eta$ at each stress level (for Weibull) or $\exp(\mu_{\log})$ (for lognormal). The P2 column is the shape parameter $\beta$, constrained to be equal across stress levels. The $relationship coefficients coef1 and coef2 define the log-linear life-stress model (Nelson 1990).

Plot Tools

The three plot_* tools run the full fitting pipeline and return an interactive plot as a plotly JSON string. Claude Code (or any MCP client) can save the string to an .html file that opens directly in a browser.

The `plot_weibull` Tool

plot_weibull accepts the same arguments as fit_weibull plus an optional show_conf flag, and returns the probability plot as a JSON string.

ptool <- ReliaPlotR:::.make_plot_wblr_tool()
json_str <- ptool(failures = c(30, 49, 82, 90, 96))
nchar(json_str)  # plotly JSON string length
#> [1] 12771

The `plot_alt` Tool

plot_alt accepts the same arguments as fit_alt and returns a named list with two JSON strings: probability_plot (one fitted line per stress level) and life_stress_plot (Arrhenius or Power Law relationship).

ptool_alt <- ReliaPlotR:::.make_plot_alt_tool()
result_plots <- ptool_alt(
  stresses      = c(300, 350, 400),
  failures_json = "[[248,456,528,731,813,537],[164,176,289],[88,112,152]]",
  dist          = "weibull",
  alt_model     = "arrhenius"
)

nchar(result_plots$probability_plot)
#> [1] 74545
nchar(result_plots$life_stress_plot)
#> [1] 95580

The `plot_rga` Tool

plot_rga fits a Crow-AMSAA model to failure count data and returns the cumulative failure plot.

ptool_rga <- ReliaPlotR:::.make_plot_rga_tool()
json_rga <- ptool_rga(
  times    = c(100, 200, 300, 400, 500),
  failures = c(  2,   1,   3,   1,   2)
)
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
nchar(json_rga)
#> [1] 5254

How the Tools Work

Each MCP tool wraps the same fitting pipeline used by the corresponding plotly_* function:

fit_weibull:    wblr() -> wblr.fit() -> wblr.conf() -> tidy_wblr()    -> named list
fit_alt:     alt.make() -> alt.parallel() -> alt.fit() -> tidy_alt()  -> named list
plot_weibull:   wblr() -> wblr.fit() -> wblr.conf() -> plotly_wblr()  -> plotly JSON
plot_alt:    alt.make() -> alt.parallel() -> alt.fit() -> plotly_alt()
                                                       -> plotly_rel() -> plotly JSON × 2
plot_rga:       rga()                                 -> plotly_rga()  -> plotly JSON

The accessor functions (tidy_wblr(), tidy_alt(), tidy_rga()) can also be called directly from R to extract parameter estimates from already-fitted model objects without going through MCP.

ReliaGrowR MCP Tools

The ReliaGrowR server exposes eight tools for reliability growth analysis and repairable systems. Register it with claude mcp add -s user reliagrowR -- Rscript -e "ReliaGrowR::rga_mcp_server()".

Tool	Arguments	Returns
`rga`	`times`, `failures`, `times_type`, `method`	λ, β, growth rate, log-likelihood, AIC, BIC
`nhpp`	`time`, `event`, `model_type`, `method`	model type, method, fitted parameters, GOF stats
`duane`	`times`, `failures`, `conf_level`	slope, intercept, GOF stats
`mcf`	`id`, `time`, `event`, `conf_level`	time points, MCF values, confidence bounds
`predict_rga`	`times`, `failures`, `forecast_times`, `method`, `conf_level`	forecast cumulative failures with bounds
`predict_duane`	`times`, `failures`, `forecast_times`, `conf_level`	forecast MTBF with bounds
`rdt`	`target`, `mission_time`, `conf_level`, `beta`, `f`, `n`, `test_time`	required sample size or test duration
`gof_rga`	`times`, `failures`, `method`	Cramér-von Mises (W²) and Kolmogorov-Smirnov (D) statistics

MCP Tools

Overview

Installation

Registering with Claude Code

The `fit_weibull` Tool

Arguments

Example

The `fit_alt` Tool

Arguments

Example

Plot Tools

The `plot_weibull` Tool

The `plot_alt` Tool

The `plot_rga` Tool

How the Tools Work

ReliaGrowR MCP Tools

References

See Also

MCP Tools

Overview

Installation

Registering with Claude Code

The fit_weibull Tool

Arguments

Example

The fit_alt Tool

Arguments

Example

Plot Tools

The plot_weibull Tool

The plot_alt Tool

The plot_rga Tool

How the Tools Work

ReliaGrowR MCP Tools

References

See Also

The `fit_weibull` Tool

The `fit_alt` Tool

The `plot_weibull` Tool

The `plot_alt` Tool

The `plot_rga` Tool