_ registry / mcp http-sse

discreterate-v1

https://discreterate.com

Registry code: 2d1d677c801aaaa9

api record

DiscreteRate's public MCP. Two kinds of tool: (1) VERIFIED-REFERENCE tools — the seven run_* sim tools (Fast-Slow Drain, Hamburger Duo, Valdez Tanker, Vegetable Plant, Chocolate Processing, Bottling Line SKU capacity, Tissue Line) return canonical curated numbers from real DRS engine runs, and five explain_*/catalog tools return educator-flavored prose. (2) run_showcase (present only when the live engine is wired in) runs the real engine LIVE for parameters you choose and returns its verbatim run envelope — real output for that exact configuration, NOT a canonical reference number.…

endpoint
https://discreterate.com/mcp/v1
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
unknown
uptime
—
latency
—

last good check

priced tools
0

of 14 tools

_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 14 tools
14 never probed 0 of 14 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • explain_three_primitives unknown never probed

    Return a focused write-up of the three DRS modeling primitives: Constraint (rate-limiter), Buffer (accumulated state), Interrupt (stoppage). Use this when the user asks specifically about modeling primitives or how to spell a system in DRS. Deterministic text.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • explain_paradigm_integration unknown never probed

    Return an explainer of paradigm integration — how DRS handles systems with both flows and items via F2I (Flow-to-Item) and I2F (Item-to-Flow) primitives. Use this when the user asks about Valdez-Tanker-style mixed-paradigm systems or 'how do flows and items coexist'. Deterministic text.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • run_fast_slow_drain unknown never probed

    Run the Fast-Slow Drain (FSD) demo — Damiron-Nastasi 2008 oscillating tank. The canonical DRS-vs-DES event-count demonstration. Returns engine output including the event counts (DES vs DRS), tank-level trace, and cycle summary. ANTI-FABRICATION: numbers come from a real DRS engine run; quote verbatim, don't recall from training data.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "simulation_minutes": {
      "type": "integer",
      "default": 100,
      "maximum": 1000,
      "minimum": 10,
      "description": "Total simulation horizon in minutes. Default 100. Range 10-1000."
    }
  }
}
    arguments 12 lines
  • run_hamburger_duo unknown never probed

    Run the Hamburger Duo (HAM) demo — Andy Siprelle's 5-stage finite-source line, executed as both DES and DRS implementations on the same model so the event-count and throughput numbers can be compared apples-to-apples. Returns engine output for the side-by-side run. ANTI-FABRICATION: numbers come from a real engine run; quote verbatim.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "simulation_days": {
      "type": "integer",
      "default": 7,
      "maximum": 30,
      "minimum": 1,
      "description": "Days to simulate. Default 7. Range 1-30."
    }
  }
}
    arguments 12 lines
  • run_valdez_tanker unknown never probed

    Run the Valdez Tanker (VALD) demo — Koelling-Remy 1983 Alaska Pipeline model, the paradigm-integration motivator. Crude flows continuously into the Valdez Marine Terminal storage tank (Flow); tankers arrive discretely to drain it (Item); DRS handles both via F2I / I2F transitions. Returns engine output including tanker arrival/departure events and tank-level trace. ANTI-FABRICATION: numbers come from a real engine run; quote verbatim.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "simulation_days": {
      "type": "integer",
      "default": 30,
      "maximum": 90,
      "minimum": 1,
      "description": "Days to simulate. Default 30. Range 1-90."
    }
  }
}
    arguments 12 lines
  • run_vegetable_plant unknown never probed

    Run the Vegetable Plant (VEG) demo — a Plant Builder distribution-control model. Two Making lines feed five Packing lines through eight surge bins; a DRS rate solver splits and rebalances the flow across the bins as the plant works through its campaign schedule. Returns the plant rollup (utilization, campaigns, active window), per-product goal attainment, per-system campaign timelines, and final surge-bin / delivered levels. ANTI-FABRICATION: numbers come from a real Plant Builder engine run; quote verbatim.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • explain_discrete_rate_simulation unknown never probed

    Return a textbook-tier explainer of Discrete Rate Simulation: how it differs from DES and CT, the three primitives (Constraint / Buffer / Interrupt), paradigm integration via F2I / I2F. Use this for 'what is DRS?' / 'how is this different from DES?' / 'where does DRS fit in the simulation landscape?' style questions. Deterministic text — no engine call, no RNG.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • run_chocolate_processing unknown never probed

    Run the Chocolate Processing (CHOC) demo — Plant Builder's joint DES↔DRS bridge. Three systems in series (Bean Processing → Cocoa Powder → Chocolate): DES schedules campaigns and injects equipment failures, a DRS rate solver carries the continuous flow, a bridge couples them. Exercises all 7 controllers + Goal blocks. Returns the plant rollup (schedule occupancy vs busy utilization, total downtime, campaigns), per-product attainment, and per-system campaign timelines with downtime. ANTI-FABRICATION: numbers come from a real Plant Builder engine run; quote verbatim.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • run_sku_capacity unknown never probed

    Run the Bottling Line / SKU-capacity (SKU) demo — a sim-foundation parameter-set example. One 5-machine bottling line run for several products (SKUs as parameter sets). Returns, per SKU, OEE (identical ~55% — time-based interrupts) and indexed real output (swings >3x: 100 / 50 / 30 / 42) plus the pacing machine. Shows you can't read per-SKU capacity off OEE. ANTI-FABRICATION: numbers come from a real sim-foundation engine run (indexed/anonymized); quote verbatim.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • run_tissue_line unknown never probed

    Run the Tissue Line (TIS) demo — a sim-foundation parameter-set example. One tissue line (Reel supply → Converter → Winder), three strategic decisions (each a parameter set): bypass converter / run converter / add storage tower. Returns per-decision throughput as % of nameplate (75.4 / 75.4 / 73.4), the binding bottleneck (the upstream parent-reel supply in all three), and converter/storage detail. Shows the downstream decision barely moves throughput — invest at the constraint. ANTI-FABRICATION: numbers come from a real sim-foundation engine run (indexed/anonymized); quote verbatim.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • list_drs_demos unknown never probed

    List the seven DRS demos (Fast-Slow Drain · Hamburger Duo · Valdez Tanker · Vegetable Plant · Chocolate Processing · Bottling Line SKU capacity · Tissue Line). Each is reproducible against the engine via the run_* tools. Use this to discover what's available before calling describe_demo or a run_* tool.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • describe_demo unknown never probed

    Full per-demo write-up: history, what it teaches, what to expect from the run_* output. Use this to ground the user before triggering a sim run, or to explain WHY the demo exists when the user asks a conceptual question about it.

    mcp-tool

    {
  "type": "object",
  "required": [
    "demo"
  ],
  "properties": {
    "demo": {
      "enum": [
        "fast_slow_drain",
        "hamburger_duo",
        "valdez_tanker",
        "vegetable_plant",
        "chocolate_processing",
        "sku_capacity",
        "tissue_line"
      ],
      "type": "string",
      "description": "Which DRS demo to act on. See list_drs_demos for the catalog."
    }
  }
}
    arguments 21 lines
  • explain_des_vs_drs_event_complexity unknown never probed

    Return a focused write-up of the event-count complexity differences between DES and DRS, with the worked Fast-Slow Drain numbers (Continuous ~thousands vs DES ~500 vs DRS 10 events for the same 100-minute model). Use this when the user wants the practitioner-visible payoff of DRS — the 50× event-count reduction at the boundary-transition layer. Deterministic text.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • run_showcase unknown never probed

    LIVE EXPERIMENT — run a DRS demo against the real 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: pick a demo_id and dial its knobs (e.g. `stop_time` run length, or the MTBF/MTTR/goal knobs on the plant demos) to see the real numbers for that exact configuration. IMPORTANT: a run_showcase result is NOT a verified reference number — unlike the run_* tools (run_fast_slow_drain / run_hamburger_duo / run_valdez_tanker / run_vegetable_plant / run_chocolate_processing), which return curated, canonical reference values. Present run_showcase output as a live experiment result for the parameters passed; don't blend it with the curated reference numbers. Quote any figures verbatim; do not round, average, or derive.

    mcp-tool

    {
  "type": "object",
  "required": [
    "demo_id"
  ],
  "properties": {
    "knobs": {
      "type": "object",
      "description": "Optional parameters as a map of name:number. fast_slow_drain / hamburger_duo accept `stop_time` (run length in minutes, 1–100000). valdez_tanker accepts `duration_days` (run length in days, 1–365; it drives the circulating-ship items loop). vegetable_full accepts `making_goal` (units, 0–100000), `making_mtbf` / `making_mttr` / `packing_mtbf` / `packing_mttr` (hours, 0–1000). chocolate_processing accepts `bp_goal` (units, 0–100000), `breaker_mtbf` / `breaker_mttr` (hours, 0–1000), `changeover_delay` (hours, 0–48). Unknown names are rejected; out-of-range values are clamped by the engine."
    },
    "demo_id": {
      "enum": [
        "fast_slow_drain",
        "hamburger_duo",
        "valdez_tanker",
        "vegetable_full",
        "chocolate_processing"
      ],
      "type": "string",
      "description": "Which DRS demo to run live against the real engine."
    }
  }
}
    arguments 23 lines
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