Connect ReliaStats and your agent gets 11 tools, among them compute_availability, describe_bottling_line, explain_advanced_reliability_patterns. It answers at https://reliastats.com/mcp/v1 over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 10 of 10 times since 5 September 2026; the last check, on 18 October 2026, succeeded.
Reliability statistics — Weibull/lognormal fitting, MTBF/MTTR, availability, system composition.
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
compute_availability | 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 si Inputs: mtbf, mttr |
describe_bottling_line | 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 Inputs: section |
explain_advanced_reliability_patterns | 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 r |
explain_distributions_for_reliability | 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 Exponent |
explain_pi_vs_ci_for_validation | 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 ou |
explain_reliability_basics | 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 reliabilit |
interpret_weibull_shape | 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-o Inputs: beta, eta |
list_paired_models | 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 sing |
recommend_distribution | 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. Keywor Inputs: symptoms |
system_reliability | 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 rea Inputs: components, structure |
weibull_summary | 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 th Inputs: beta, eta, evaluateAtT |
Every block below points at https://reliastats.com/mcp/v1. Pick your client and copy.
claude mcp add --transport http reliastats https://reliastats.com/mcp/v1
{
"mcpServers": {
"reliastats": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://reliastats.com/mcp/v1"]
}
}
}{
"mcpServers": {
"reliastats": {
"url": "https://reliastats.com/mcp/v1"
}
}
}{
"servers": {
"reliastats": {
"type": "http",
"url": "https://reliastats.com/mcp/v1"
}
}
}{
"context_servers": {
"reliastats": {
"url": "https://reliastats.com/mcp/v1"
}
}
}{
"mcpServers": {
"reliastats": {
"serverUrl": "https://reliastats.com/mcp/v1"
}
}
}{
"mcpServers": {
"reliastats": {
"url": "https://reliastats.com/mcp/v1"
}
}
}name: ReliaStats
version: 0.0.1
schema: v1
mcpServers:
- name: ReliaStats
type: streamable-http
url: https://reliastats.com/mcp/v1{
"mcpServers": {
"reliastats": {
"httpUrl": "https://reliastats.com/mcp/v1"
}
}
}{
"mcpServers": {
"reliastats": {
"type": "http",
"url": "https://reliastats.com/mcp/v1"
}
}
}{
"mcpServers": {
"reliastats": {
"url": "https://reliastats.com/mcp/v1"
}
}
}goose://extension?url=https%3A%2F%2Freliastats.com%2Fmcp%2Fv1&type=streamable_http&id=reliastats&name=ReliaStats
It answered our last check on 18 October 2026 in 263 ms. Over 10 checks since 5 September 2026 it answered 10. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by reliastats.com and served from reliastats.com, the vendor's own domain: https://reliastats.com/mcp/v1.
11, as returned by the server's own tools/list on 31 August 2026: compute_availability, describe_bottling_line, explain_advanced_reliability_patterns, explain_distributions_for_reliability, explain_pi_vs_ci_for_validation, explain_reliability_basics, interpret_weibull_shape, list_paired_models, and 3 more.
It answered our last check on 18 October 2026 in 263 ms. Over 10 checks since 5 September 2026 it answered 10.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://reliastats.com/mcp/v1. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: com.reliastats. Endpoint host: reliastats.com. ARD identifier: urn:air:com.reliastats:mcp:public.
First indexed 31 August 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.