SCModeling

com.scmodelingPublished by scmodeling.comOfficial publisherv1.0.1Updated Oct 25, 2026

Supply-chain network design via simulation, optimization, and greenfield analysis.

VerifiedStreamable HTTPWeb executableData & AnalyticsBusiness & Commerce

Overview

AI-generated overview

A remote MCP server for supply-chain network design: it runs sample simulations and returns precomputed optimization and greenfield results, plus reference text

What you can do
List and run bundled sample supply-chain simulations, returning metrics, inventory time series, orders, shipments, routing and BOM data from a discrete-event engine. List optimization demos, describe their controls and scenarios, and fetch precomputed results with sourcing, production, transport and facility decisions. List greenfield demos, describe them, and fetch precomputed facility-location results with sited DCs, customer assignments and scores. Read reference text on simulation concepts, mixed-integer optimization and greenfield location math.
When to use it
Useful when you want an assistant to explain supply-chain simulation, optimization or greenfield location concepts, or to walk through bundled sample models and demos with real engine numbers rather than invented figures. It is not for modeling your own network, since all demos are precomputed fixtures.
Requirements
A remote streamable HTTP endpoint at scmodeling.com; no packages, environment variables or headers are declared, and no authentication is declared. Network access to the endpoint is needed.
Before you install
Results are sample-only fixtures, not analysis of your own data; the server's own text says to use it for real optimization elsewhere. The tools ask the assistant to quote returned numbers verbatim, which is a presentation demand rather than a data guarantee. No authentication is declared, so do not send confidential supply-chain data.

Installation

In SourceWeft

  1. Open SCModeling in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/com.scmodeling%2Fpublic/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "scmodeling": {
      "type": "http",
      "url": "https://scmodeling.com/mcp"
    }
  }
}

Tools

12

run_simulation

Run supply-chain DES simulation

Run a supply-chain simulation on a bundled SCModeling sample model (sdi-db). Returns metrics, inventory time-series, orders, shipments, routing and BOM. ANTI-FABRICATION: the returned numbers come from a real discrete-event simulation run on the sc-sim engine. Quote them VERBATIM in your reply. Do not round, estimate, average, or compute derived figures from training-data recall. If the user asks a follow-up about the same model, re-call this tool rather than recalling numbers from earlier in the conversation.

Parametersmodel_idrequired

list_models

List bundled sample models

List the bundled SCModeling sample supply-chain models. Returns a catalog with each model's id and a short description. Use this before run_simulation to know which model_id values are valid.

Takes no parameters.

get_sc_theory

Simulation theory primer

Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

Takes no parameters.

explain_optimization

Network optimization primer

Reference text on supply-chain network optimization — mixed-integer programming (MIP), the structure of decision variables and constraints, the objective function for landed-cost minimization, and the common problem classes (facility selection, sourcing, flow constraints, multi-period, BOM/production, multi-objective). Also covers when to reach for optimization vs simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does network optimization work' question. ChiAha's AMOS optimizer (open-source, Odin, GLOP/CBC via OR-Tools) powers the Tariff and Coffee Co-pack demos on the sandbox.

Takes no parameters.

explain_greenfield

Greenfield analysis primer

Reference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.

Takes no parameters.

explain_simulation

Supply-chain simulation primer

Reference text on supply-chain simulation — how a discrete-event model of a network behaves through time. Covers event-driven execution (future-event list, the consume / check-inventory / place-order / fill / ship / deliver vocabulary), why inventory POSITION rather than on-hand stock drives reordering, which KPIs the engine reports versus which this site derives from the raw order and shipment records, when to reach for simulation, for optimization, and for both together, and the honest limitations of these runs (single deterministic replication, cached results, warm-up inside the reported window, fixed sample parameters). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does the simulation work', 'why did this DC stock out', or 'should I simulate or optimize' question rather than asking for a number; call run_simulation when they want actual figures.

Takes no parameters.

list_opt_demos

List optimization demos

List the bundled SCModeling optimization demos. Returns id + label + one-line summary for each (Tariff, Coffee Co-pack, SSO Basic). Use this before describe_opt_demo or get_opt_result to know which demo_id values are valid. All demos are precomputed sample-only fixtures — for optimization on real client data, the SCModeling desktop tool is the product.

Takes no parameters.

describe_opt_demo

Describe optimization demo

Full detail on one optimization demo — controls, available scenario keys, sites, fixed parameters, citations, and the key finding the demo illustrates. Use this before get_opt_result to know what scenario_key values are accepted.

Parametersdemo_idrequired

get_opt_result

Get optimization scenario result

Get the precomputed result for one scenario of an optimization demo. Returns the verbatim engine output JSON (AMOS for tariff/coffee, SSO output for sso-basic) including the optimal sourcing/production/transport decisions, costs, and any open/close facility variables. ANTI-FABRICATION: every numeric result is verbatim from the optimization engine that ran offline — quote them in your reply, do not round or recompute. Call describe_opt_demo first to learn valid scenario_key formats for each demo.

Parametersdemo_idrequiredscenario_key

list_greenfield_demos

List greenfield demos

List the bundled SCModeling greenfield demos. Returns id + label + one-line summary. Currently one demo (US, 189 customer points). Use this before describe_greenfield_demo or get_greenfield_result.

Takes no parameters.

describe_greenfield_demo

Describe greenfield demo

Full detail on one greenfield demo — region, customer count, available dc_count values, and the score-curve elbow finding. Use this before get_greenfield_result to know what dc_count values are precomputed.

Parametersdemo_idrequired

get_greenfield_result

Get greenfield DC siting

Get the precomputed result for one DC count of a greenfield demo. Returns sited DCs (lat/lon + city/state, nearest-city snapped), customer-to-DC assignments, and the score for that DC count. ANTI-FABRICATION: every result is verbatim engine output from greenfield-cli — quote them in your reply, do not round or fabricate cities.

Parametersdemo_idrequireddc_countrequired

Version history

1
  1. v1.0.1LatestSep 16, 2026

Score

83% of the applicable checks met

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  • Documentation15 pts

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  • LicenseNot applicable

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