MCP server

ReliaStats Official

com.reliastats/public

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Reliability statistics — Weibull/lognormal fitting, MTBF/MTTR, availability, system composition.

Source

At a glance

Type

MCP server

Publisher

reliastats.com (verified by the MCP Registry)

Status

Working at last check

Tools

11

Token cost

About 2,770 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http public https://reliastats.com/mcp/v1

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.public]
url = "https://reliastats.com/mcp/v1"

Gemini CLI

{"mcpServers":{"public":{"httpUrl":"https://reliastats.com/mcp/v1"}}}

Cursor

{"mcpServers":{"public":{"url":"https://reliastats.com/mcp/v1"}}}

VS Code

{"servers":{"public":{"type":"http","url":"https://reliastats.com/mcp/v1"}}}

Antigravity

agy mcp add public https://reliastats.com/mcp/v1

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"public","server_url":"https://reliastats.com/mcp/v1","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"public","server_url":"https://reliastats.com/mcp/v1"}

Tools

• compute_availability ~224 tokens
  Given MTBF and MTTR (same time unit), return steady-state availability A = MTBF / (MTBF + MTTR). One-line closed-form, but worth a dedicated tool so LLMs don't fumble the identity (the most common mistake is conflating MTBF with MTTF and silently inflating availability by the MTTR). Use whenever a u
• describe_bottling_line ~339 tokens
  Return the full worked-example doc for the bottling-line paired model — topology (5 machines: Filler/Capper/Labeler/Case Packer/Palletizer, 100 bottles/min, Weibull(30,1) TTF + Weibull(5,1) downtime at the Constraint-Level rollup), the two tracks (CT rollup vs LEDS-Level drill-down to 36 named failu
• explain_advanced_reliability_patterns ~191 tokens
  Return a textbook-tier explainer of advanced reliability patterns: censored data (right/left/interval — the rule not the exception), Maximum Likelihood Estimation, Goodness-of-Fit tests (Anderson-Darling favored over KS for tail-sensitive reliability work), the Confidence-Interval vs Prediction-Inte
• explain_distributions_for_reliability ~216 tokens
  Return a textbook-tier distribution zoology for reliability work: why Weibull is the default, the shape-parameter β table mapping β-ranges to physical failure modes (β<1 infant mortality, β=1 random, β>1 wearout), when to reach for Exponential / Lognormal / Normal / Gamma, and practitioner heuristic
• explain_pi_vs_ci_for_validation ~196 tokens
  Return the specific explainer for the ReliaStats Interrupt Validation scatter chart's red y=x / blue 95% Prediction Interval / teal 99% Confidence Interval reference lines. Use when a user asks 'what do the bands mean' / 'why is my point outside the blue line' / 'how do I read the validation scatter
• explain_reliability_basics ~198 tokens
  Return a textbook-tier explainer of reliability fundamentals: the four reliability functions R(t)/F(t)/f(t)/h(t), MTBF vs MTTF vs MTTR, the availability identity A = MTBF/(MTBF+MTTR), the bathtub curve, and series/parallel system reliability. No inputs. Use when a user asks 'what is reliability theo
• interpret_weibull_shape ~337 tokens
  Given a Weibull shape parameter β (and optionally the characteristic-life parameter η), return a plain-language interpretation: which bathtub-curve regime β implies (infant mortality / random / wearout), what action that suggests (process-of-care / steady-state monitoring / maintenance scheduling),
• list_paired_models ~222 tokens
  Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model
• recommend_distribution ~283 tokens
  Given a free-text symptom description (e.g. 'manufacturing burn-in', 'bearing wearout under variable load', 'cosmic-ray bit flips'), return an ordered shortlist of distribution candidates with a one-line rationale per recommendation. Keyword-matched against a curated dictionary; ALWAYS treat output
• system_reliability ~262 tokens
  Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (ser
• weibull_summary ~302 tokens
  Given Weibull two-parameter (β, η), return all the closed-form summary statistics: MTTF (η·Γ(1+1/β)), B10 / B50 / B90 life, characteristic life (just η, surfaced explicitly), and — if evaluateAtT supplied — R(t), F(t), and hazard h(t) at that time. Pure-math, fully deterministic. Use when the user h

Browse

• Website monitoring
• Booking and scheduling
• Legal research
• Running code

Related MCP servers

• NeuroRank
  Esports cognitive-combine data over MCP: free cohort stats, paid player & team scouting reports.
• Glimind
  Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
• Cricket
  Live cricket incl. US Minor League, Kalshi prices, and a win model fitted on 10,847 matches.
• Optionscanner
  Free options calculators and the Optionscanner Learn library for any AI assistant. No account.
• Dominionobservatory Observatory
  Runtime behavioral trust scoring for MCP servers. Check reliability before calling unknown tools.
• Imagine AI Benchmark
  LinkedIn content benchmark for 900+ B2B companies and their teams, with an open scoring method.
• Designers
  Checks, prompts and skills for designers working with AI: contrast, a11y, heuristics, critique.
• AgentTrust — Pre-Invocation AI Agent Trust & Reliability Checks
  Check an AI agent endpoint before you call it: read-only, no API key, returns a trustDecision.

Badge for your README

[![AptLynx](https://aptlynx.net/badge/com.reliastats/public.svg)](https://aptlynx.net/servers/com.reliastats/public)

Listing text is written by the maker; AptLynx lists tools and does not vouch for them. Find another tool · Browse by topic · For agents