SCModeling

skills: {'id': 'run_simulation', 'name': 'run_simulation', 'description': '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.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'list_models', 'name': 'list_models', 'description': "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.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'get_sc_theory', 'name': 'get_sc_theory', 'description': "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.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'explain_optimization', 'name': 'explain_optimization', 'description': "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.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'explain_greenfield', 'name': 'explain_greenfield', 'description': "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.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'list_opt_demos', 'name': 'list_opt_demos', 'description': '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.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'describe_opt_demo', 'name': 'describe_opt_demo', 'description': '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.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'get_opt_result', 'name': 'get_opt_result', 'description': '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.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}; uptime_30d 1.0%; p95 116.8ms; conformance: pass

How to connect

https://scmodeling.com/mcp

curl -X POST https://scmodeling.com/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}'