# Discreterate Public MCP server

Source: https://glimind.com/server/mcp-registry%2Fcom.discreterate%2Fpublic
Registry id: `mcp-registry/com.discreterate/public`
Status: live (checked 2026-10-26T22:39:26.726Z)

## Verdict

Discreterate Public MCP server answered 99.65% of Glimind's liveness checks over 30 days, better than 18% of MCP servers, with 1169 ms median handshake and 23 outages in 90 days. Its agent-readiness grade is A (96/100) across 14 tools, costing an agent about 2,303 tokens to load. Checked every few minutes; no tool is ever called.

## Measurements (30 days)

| Metric | Value | Better than |
|---|---|---|
| Uptime, 30 days | 99.65% | 18% of MCP servers |
| Median handshake latency | 1169 ms | 15% of MCP servers |
| p95 handshake latency | 5611 ms | — |
| Checks in 30 days | 4,900 | — |
| Outages, 90 days | 23 | — |
| Median recovery | 7 min | — |
| Agent-readiness score | 96/100 (A) | 82% of MCP servers |
| Tools | 14 | — |
| Tool-list size for an agent | ~2,303 tokens | 50% of MCP servers |
| Days observed | 29 | — |

## Install

```json
{
  "mcpServers": {
    "discreterate-public-mcp-server": {
      "type": "http",
      "url": "https://discreterate.com/mcp/v1"
    }
  }
}
```

Claude Code:

```sh
claude mcp add --transport http discreterate-public-mcp-server https://discreterate.com/mcp/v1
```

## Tools (14)

- `describe_demo`: 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 W
- `explain_des_vs_drs_event_complexity`: 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 ~
- `explain_discrete_rate_simulation`: Return a textbook-tier explainer of Discrete Rate Simulation: how it differs from DES and CT, the three primitives (Constraint / Buffer / Interrupt), paradigm i
- `explain_paradigm_integration`: 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 th
- `explain_three_primitives`: Return a focused write-up of the three DRS modeling primitives: Constraint (rate-limiter), Buffer (accumulated state), Interrupt (stoppage). Use this when the u
- `list_drs_demos`: List the seven DRS demos (Fast-Slow Drain · Hamburger Duo · Valdez Tanker · Vegetable Plant · Chocolate Processing · Bottling Line SKU capacity · Tissue Line). 
- `run_chocolate_processing`: Run the Chocolate Processing (CHOC) demo — Plant Builder's joint DES↔DRS bridge. Three systems in series (Bean Processing → Cocoa Powder → Chocolate): DES sched
- `run_fast_slow_drain`: Run the Fast-Slow Drain (FSD) demo — Damiron-Nastasi 2008 oscillating tank. The canonical DRS-vs-DES event-count demonstration. Returns engine output including 
- `run_hamburger_duo`: 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
- `run_showcase`: LIVE EXPERIMENT — run a DRS demo against the real engine with parameters you choose, and get its verbatim run envelope (metadata, execution stats, metrics, deta
- `run_sku_capacity`: 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 paramete
- `run_tissue_line`: Run the Tissue Line (TIS) demo — a sim-foundation parameter-set example. One tissue line (Reel supply → Converter → Winder), three strategic decisions (each a p
- `run_valdez_tanker`: Run the Valdez Tanker (VALD) demo — Koelling-Remy 1983 Alaska Pipeline model, the paradigm-integration motivator. Crude flows continuously into the Valdez Marin
- `run_vegetable_plant`: 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 s

Measured by Glimind (https://glimind.com): MCP handshake + tool list only, no tool is ever called. CC BY 4.0.