mcp server

ReliaSim

Reliability and bottleneck simulation for manufacturing lines; run experiments, sweep buffers.

Description as published by the maintainer. Source

• version 1.0.0
• archived

archived — The linked repository returns 404. It was deleted, renamed or made private.

What this server can do

8 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

compare_chapters(chapter_a, chapter_b)

Side-by-side comparison of two chapters — tracks, topology, OEE, throughput, headline bottleneck. Output is sim-derived (no interpretation drift). Use for 'how does X compare to Y?' / 'what's the difference between Constraint-Level and LEDS-Level on the same model?' / 'what changes when we add buffers?' questions. ANTI-FABRICATION: per-chapter OEE/throughput numbers are real reference values; the side-by-side delta is computed from them, not estimated. Quote VERBATIM. Required: chapter_a, chapter_b.

explain_concept(concept)

Definitional primer for ReliaSim's framework concepts — Constraint, Buffer, Interrupt, Converter, cascading losses, OEE, Gain/Loss methodology, Buffer Tradeoff. Returns bundled theory content, NOT interpretation of any specific simulation run. Use for 'what is X?' / 'how does X work?' / 'explain the framework' questions. For line-specific claims (throughput, availability, what-if), call the sim tools instead. Required: concept.

find_bottleneck(chapter)

Single Run bottleneck analysis for the selected chapter — which node has the worst availability, per-interrupt downtime split, throughput, OEE. All eight chapters return verified dys-cli sales-prototype numbers. ANTI-FABRICATION: numbers in the response are canonical reference values from real dys-cli engine runs. Quote them VERBATIM. Do not round, estimate, or recall from training data. For follow-ups about the same chapter, re-call this tool.

get_chapter_facts(chapter)

Structural facts of the selected chapter — topology, rate limits, interrupt distributions, expected efficiency. Use when the user asks about the line's configuration. ANTI-FABRICATION: rates and distributions are verified .aidos-file values. Quote VERBATIM; do not estimate or substitute training-data recall.

get_chapter_narrative(chapter)

Long-form narrative for the selected chapter — what the chapter adds to the complexity ladder and the key teaching point. Use when the user asks 'walk me through this' or wants the conceptual primer. Pure prose, no numerical claims; safe to summarize.

run_buffer_tradeoff(buffer, chapter)

Buffer Tradeoff experiment — sweep a buffer's capacity from 50 → 10,000 units, measure throughput gain. Shows the diminishing-returns elbow for buffer sizing. Only defined on `bs4-ct` and `bs4-leds`; each chapter has THREE inline buffers with different placements (pass `buffer` id to pick one). Compare CT vs LEDS on the same slot to see why interrupt-detail level changes buffer ROI math (e.g. b3: CT +23.7% vs LEDS +64.2%). Use when the user asks 'how big should the buffer be?' / 'do buffers help on this line?' / 'which buffer position gives the most gain?' / 'what's the diminishing-returns point?'. ANTI-FABRICATION (CRITICAL): the specific tradeoff numbers (e.g. CT +23.7% vs LEDS +64.2%) are sweep-derived reference values. Quote VERBATIM in your reply; do NOT recall similar percentages from training data — every buffer position has different math.

run_gain_loss(chapter)

Gain/Loss experiment — disable each interrupt one at a time, measure production recovered. Reveals the ACTUAL impact of each failure mode (Gain ≠ Loss: removing one lets others fire more often). Available on `bs1-leds`, `bs3-leds`, `bs4-ct`, `bs4-leds`. Use when the user asks 'what if we fixed X?' / 'which interrupt matters most if we actually fixed it?' / 'show me the Pareto'. ANTI-FABRICATION: per-interrupt recovered-production numbers come from real dys-cli runs. Quote VERBATIM; the Gain ≠ Loss interaction is exactly the kind of figure LLMs are prone to fabricate — don't.

run_showcase(knobs, demo_id)

LIVE experiment — run a bottling-line demo against the real ReliaSim engine with parameters you choose, and get its verbatim run envelope (metadata, execution stats, metrics, details). This is the only tool that COMPUTES fresh output: dial `duration_days` (or buffer capacities on the bs4 demos) and see the real numbers for that exact configuration. IMPORTANT: a run_showcase result is NOT a verified reference number — it is live output for the parameters you passed. Label it as an experiment result, not a canonical figure, and don't blend it with the curated reference numbers. For the canonical, verified OEE/throughput/bottleneck values use find_bottleneck / run_gain_loss / run_buffer_tradeoff instead. Quote any figures verbatim; do not round, average, or derive. Required: demo_id.

Last successful function declaration observed on 2026-08-13. Source: https://reliasim.com/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://reliasim.com/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/chiaha-ai/reliasim-site
• Remote endpoint (streamable-http) — https://reliasim.com/mcp

Also from chiaha-ai

• DiscreteRate — repository gone
  Run DRS demos (Fast-Slow Drain, Hamburger Duo, Valdez Tanker) and explore the paradigm.
• QSimHealth — repository gone
  Healthcare staffing simulator — ED, walk-in clinic, and appointment office DES tools.
• QueueSim — repository gone
  Run M/M/c queue simulations and four scenarios (call center, ER, coffee shop, single server).
• ReliaStats — repository gone
  Reliability statistics — Weibull/lognormal fitting, MTBF/MTTR, availability, system composition.