scmodeling

endpoint

https://scmodeling.com/mcp

protocol

streamable-http ·2024-11-05

authentication

none observed

public key

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karma

0 · newcomer

Is scmodeling live?

Yes — it answered the hub's last check (checked 2h ago). It answered 100% of checks over the last 30 days.

Is scmodeling free to use?

Yes — the hub reached it with no key and no payment.

What tools does scmodeling have?

12 tools: get_greenfield_result, get_opt_result, run_simulation, describe_opt_demo, describe_greenfield_demo, list_opt_demos, get_sc_theory, list_models, ….

Is scmodeling safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

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.

• list_opt_demos reads open 12h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• get_sc_theory reads open 12h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• list_models reads open 16h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• explain_simulation reads open 12h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• list_greenfield_demos reads open 9h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• explain_optimization reads open 16h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• explain_greenfield reads open 16h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• get_greenfield_result reads unknown 6h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "demo_id",
      "dc_count"
    ],
    "properties": {
      "demo_id": {
        "enum": [
          "us"
        ],
        "type": "string",
        "description": "Which greenfield demo"
      },
      "dc_count": {
        "enum": [
          "2",
          "3",
          "4",
          "5",
          "6",
          "7",
          "8"
        ],
        "type": "string",
        "description": "Number of DCs to site (2-8)"
      }
    }
  }

  arguments 29 lines copy
• get_opt_result reads unknown 6h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "demo_id"
    ],
    "properties": {
      "demo_id": {
        "enum": [
          "tariff",
          "coffee",
          "sso-basic"
        ],
        "type": "string",
        "description": "Which optimization demo"
      },
      "scenario_key": {
        "type": "string",
        "description": "Scenario key within the demo. Format varies per demo — call describe_opt_demo for the exact valid keys before guessing. Tariff uses 'APAC=<N>' where N is one of 0, 7.5, 25, 50, 100. Coffee uses '<configKey>|DSL=<N>' where configKey is T/TA/TS/TAS and N is 20-70 in steps of 5 (10c units of $/gal). sso-basic is single-scenario; scenario_key is ignored."
      }
    }
  }

  arguments 21 lines copy
• run_simulation reads unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "model_id"
    ],
    "properties": {
      "model_id": {
        "enum": [
          "simple-sc-demo",
          "cookie-making"
        ],
        "type": "string",
        "description": "Which sample model to simulate"
      }
    }
  }

  arguments 16 lines copy
• describe_opt_demo reads unknown 9h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "demo_id"
    ],
    "properties": {
      "demo_id": {
        "enum": [
          "tariff",
          "coffee",
          "sso-basic"
        ],
        "type": "string",
        "description": "Which optimization demo to describe"
      }
    }
  }

  arguments 17 lines copy
• describe_greenfield_demo reads unknown 9h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "demo_id"
    ],
    "properties": {
      "demo_id": {
        "enum": [
          "us"
        ],
        "type": "string",
        "description": "Which greenfield demo to describe"
      }
    }
  }

  arguments 15 lines copy

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