이 MCP로 할 수 있는 일
Calculates reliability statistics such as Weibull summaries, availability, system reliability, MTBF, and MTTR, with educational reliability guidance.
기능
도구
11
도구
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 silently inflating availability by the MTTR). Use whenever a user supplies an MTBF/MTTR pair and asks for availability. ANTI-FABRICATION: exact closed-form. Quote verbatim.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': ['mtbf', 'mttr'], 'properties': {'mtbf': {'type': 'number', 'default': 1000, 'minimum': 0, 'description': 'Mean Time Between Failures (repairable system). Same time unit as MTTR.'}, 'mttr': {'type': 'number', 'default': 10, 'minimum': 0, 'description': 'Mean Time To Repair. Same time unit as MTBF.'}}}
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 tracks (CT rollup vs LEDS-Level drill-down to 36 named failure modes), the 4 build sequences (BS1 → BS4), the file-shape mapping between ReliaSim outputs and ReliaStats modes, and a worked cross-MCP tool chain. Optional 'section' parameter narrows to one H2 section. ANTI-FABRICATION: content is sourced from docs/paired-model-bottling-line.md; every claim references the .aidos files or ChapterRegistry.fs in reliasim-site.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': [], 'properties': {'section': {'type': 'string', 'default': '', 'description': "Optional H2 section name from docs/paired-model-bottling-line.md to narrow the response. Examples: 'Topology — the line itself', 'The two tracks — Constraint-Level vs LEDS-Level', 'The four build sequences (BS1 → BS4)', 'File-shape mapping — which ReliaStats mode consumes what', 'The cross-MCP workflow — worked example'. Omit to return the full doc."}}}
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 reliability work), the Confidence-Interval vs Prediction-Interval distinction that backs the Interrupt Validation scatter, accelerated life testing (Arrhenius / inverse power law / Coffin-Manson), and Bayesian reliability. No inputs. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': [], 'properties': {}}
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 Exponential / Lognormal / Normal / Gamma, and practitioner heuristics for picking a distribution. No inputs. Use when a user asks 'which distribution should I fit' / 'what does Weibull β mean' / 'when to use Lognormal'. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md. The β-as-failure-mode interpretation is ChiAha's practitioner framing — quote verbatim; do not paraphrase.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': [], 'properties': {}}
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 outside the blue line' / 'how do I read the validation scatter'. The bands are FIXED plotting conventions — they are NOT recomputed from the loaded data; this is anti-fab by design. Text sourced from docs/reliability-theory.md (the 'Confidence intervals vs prediction intervals' sub-section of Advanced Reliability Patterns).
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': [], 'properties': {}}
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 reliability. No inputs. Use when a user asks 'what is reliability theory' / 'explain MTBF' / 'how does availability work' / 'what's a hazard rate'. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md (the canonical ChiAha reliability primer). Quote sections verbatim; do not paraphrase reliability theory from training-data recall.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': [], 'properties': {}}
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-of-care / steady-state monitoring / maintenance scheduling), and — if η provided — closed-form MTTF and B-life numbers from the Weibull formulas. Pure-math + lookup, no engine call, fully deterministic. Use when a user reports a fitted β and wants to know what to DO with it. ANTI-FABRICATION: MTTF and B-life are exact closed-form values from the two-parameter Weibull (η · Γ(1+1/β) and η · (-ln(1-p))^(1/β)). Quote them verbatim.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': ['beta'], 'properties': {'eta': {'type': 'number', 'default': 1000, 'minimum': 0, 'description': 'Optional Weibull characteristic-life parameter η, in the same time units you care about (e.g. hours). When provided, the response includes MTTF + B-life numbers.'}, 'beta': {'type': 'number', 'default': 2, 'maximum': 20, 'minimum': 0.01, 'description': 'Weibull shape parameter β (dimensionless). Typical reliability range 0.3 – 8.0.'}}}
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 single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': [], 'properties': {}}
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. Keyword-matched against a curated dictionary; ALWAYS treat output as a starting point for fitting work, not a fit. The actual fitting happens in the ReliaStats sandbox (protected/app.html). ANTI-FABRICATION: rationales are written ChiAha content; the algorithm is a deterministic substring match. Quote verbatim.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': ['symptoms'], 'properties': {'symptoms': {'type': 'string', 'default': 'bearing wearout', 'description': "Free-text description of the failure data or context — e.g. 'manufacturing burn-in', 'bearing wearout', 'cosmic-ray bit flips', 'multi-stage degradation'. Substring-matched against a keyword dictionary; returns an ordered shortlist with rationale."}}}
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 reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': ['components', 'structure'], 'properties': {'structure': {'enum': ['series', 'parallel'], 'type': 'string', 'default': 'series', 'description': "RBD structure: 'series' (all must work) or 'parallel' (at least one works)."}, 'components': {'type': 'array', 'items': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'default': [0.95, 0.95, 0.95], 'description': "Per-component reliabilities in [0, 1]. Order doesn't matter."}}}
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 that time. Pure-math, fully deterministic. Use when the user has a fit and wants the numbers downstream tools normally compute (don't recompute these from training-data recall — call this tool). ANTI-FABRICATION: every number is an exact closed-form value. Quote verbatim.
읽기 전용
멱등성
입력 스키마
{'type': 'object', 'required': ['beta', 'eta'], 'properties': {'eta': {'type': 'number', 'default': 1000, 'minimum': 0, 'description': 'Weibull characteristic life η, in your chosen time unit.'}, 'beta': {'type': 'number', 'default': 2, 'maximum': 20, 'minimum': 0.01, 'description': 'Weibull shape parameter β (dimensionless).'}, 'evaluateAtT': {'type': 'number', 'default': 500, 'minimum': 0, 'description': 'Optional time t (same unit as η) at which to also return reliability R(t), failure F(t), and hazard h(t).'}}}
기록
최근 도구 변경
추가됨
describe_bottling_line
2026년 9월 17일 12:37 PM
추가됨
list_paired_models
2026년 9월 17일 12:37 PM
추가됨
recommend_distribution
2026년 9월 17일 12:37 PM
추가됨
system_reliability
2026년 9월 17일 12:37 PM
추가됨
compute_availability
2026년 9월 17일 12:37 PM
추가됨
weibull_summary
2026년 9월 17일 12:37 PM
추가됨
interpret_weibull_shape
2026년 9월 17일 12:37 PM
추가됨
explain_pi_vs_ci_for_validation
2026년 9월 17일 12:37 PM
추가됨
explain_advanced_reliability_patterns
2026년 9월 17일 12:37 PM
추가됨
explain_distributions_for_reliability
2026년 9월 17일 12:37 PM
추가됨
explain_reliability_basics
2026년 9월 17일 12:37 PM
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