com.scmodeling/public
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Publisher-provided title
SCModeling
Publisher-provided description
Supply-chain network design via simulation, optimization, and greenfield analysis.
Summary by lookupd
Lets an agent explore saved supply-chain simulation runs, optimization scenarios, and greenfield siting results, plus primers on the…
Install
Claude Code, remote (streamable-http)
claude mcp add --transport http public https://scmodeling.com/mcp
Codex, remote (streamable-http)
codex mcp add public --url https://scmodeling.com/mcp
Cursor, remote (streamable-http) → .cursor/mcp.json
{
"mcpServers": {
"public": {
"url": "https://scmodeling.com/mcp"
}
}
}Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.
Tools (12 tools)
Publisher-provided text
list_modelsList the sample supply chain models run_simulation has a saved engine run for, with each model's id and a short description.run_simulationReturn the saved SC Builder engine run of a sample supply chain model: metrics, orders, shipments, routing and inventory series, verbatim. This server runs no eget_sc_theoryReference text from the SC Builder engine: event-driven model, ordering policies (pull, push, planned), the forecast-demand and order-point formulas, bill of maexplain_simulationPrimer on supply chain simulation: when to simulate, optimize or both, how a discrete-event network model works, which numbers the engine reports, and the limitexplain_optimizationPrimer on supply chain network optimization: mixed-integer programming, decision variables and constraints, landed-cost objectives, the common problem classes,explain_greenfieldPrimer on greenfield analysis: center-of-gravity (Weber) siting, Weiszfeld's algorithm, location-allocation for N facilities, service constraints, and when to ulist_opt_demosList the saved optimization demos (Tariff sourcing, Coffee co-pack siting, Safety Stock Optimization) with an id and summary each. Use before describe_opt_demodescribe_opt_demoDetail on one optimization demo: what it shows, its source, and every saved scenario key. Use before get_opt_result.get_opt_resultReturn the saved optimization result for one scenario of a demo, verbatim: sourcing, production and transport decisions, costs, and facility open/close decisionlist_greenfield_demosList the saved greenfield siting demos with an id and summary each. Use before describe_greenfield_demo or get_greenfield_result.describe_greenfield_demoDetail on one greenfield demo: region, customer count, the saved DC counts and the score curve. Use before get_greenfield_result.get_greenfield_resultReturn the saved greenfield result for one DC count, verbatim: the sited DCs (lat/lon, nearest city) and which customers each serves. Quote the numbers verbatim
Details
- Version: 1.0.1
- Transports: streamable-http
- Uptime (7 days): 78%
- Median latency: 166 ms
- First indexed:
- Last seen in registry:
- Website: https://scmodeling.com
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