# Supply Chain Digital Twin MCP (`ryanclinton/supply-chain-digital-twin-mcp`) Actor MCP intelligence server for supply chain digital twin detection and analysis. - **URL**: https://apify.com/ryanclinton/supply-chain-digital-twin-mcp.md - **Developed by:** [ryan clinton](https://apify.com/ryanclinton) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Supply Chain Digital Twin MCP A Model Context Protocol server that constructs a live digital twin of multi-tier supply chain networks. Fuses corporate registry, trade flow, natural hazard, geospatial, and intelligence data from **17 Apify actors** into a unified graph, then applies network science, game theory, and causal inference to simulate disruptions, optimize logistics, and forecast demand. ### Tools (8) | ## | Tool | Method | Price | |---|------|--------|-------| | 1 | `simulate_disruption_cascade` | Interdependent network percolation (Buldyrev et al. 2010) + cross-entropy importance sampling | $0.040 | | 2 | `optimize_logistics_transport` | Semi-discrete optimal transport, Kantorovich dual, Wasserstein distance | $0.040 | | 3 | `model_adversarial_interdiction` | Tri-level Stackelberg attacker-defender-attacker via Benders decomposition | $0.045 | | 4 | `estimate_supplier_survival` | Competing risks Cox proportional hazard model with cause-specific cumulative incidence | $0.035 | | 5 | `reinforce_network_resilience` | Algebraic connectivity (Fiedler λ₂) maximization via SDP relaxation | $0.040 | | 6 | `compute_input_output_impact` | Leontief inverse (I-A)⁻¹ via Neumann series, Ghosh supply-side multiplier | $0.035 | | 7 | `identify_causal_disruption_paths` | Fuzzy RDD with Imbens-Kalyanaraman optimal bandwidth selection | $0.035 | | 8 | `forecast_multi_scale_demand` | Rao-Blackwellized particle filter for hierarchical state-space decomposition | $0.030 | ### Data Sources (17 actors across 5 categories) **Corporate (3):** OpenCorporates, UK Companies House, GLEIF LEI **Trade (3):** UN COMTRADE, World Bank Indicators, Exchange Rates **Hazard (5):** USGS Earthquake, NOAA Weather, GDACS Disasters, FEMA, OpenAQ Air Quality **Spatial (1):** Nominatim Geocoder **Intelligence (5):** OFAC Sanctions, OpenSanctions, Censys, Website Changes, SAM.gov Contracts ### Mathematical Foundations #### Tool 1: Disruption Cascade — Interdependent Network Percolation Models supply chains as interdependent networks where failure in one layer triggers cascades across layers. The percolation threshold p_c marks the critical fraction of node failures that triggers a **first-order phase transition** (catastrophic network collapse). Cross-entropy importance sampling efficiently estimates rare cascade probabilities by tilting the failure distribution toward high-impact events. #### Tool 2: Logistics Transport — Semi-Discrete Optimal Transport Solves the **Kantorovich relaxation** of the Monge problem: find the transport plan π that minimizes total cost ∫c(x,y)dπ(x,y) subject to marginal constraints. The dual formulation yields shadow prices (Lagrange multipliers) for each capacity constraint. The **Wasserstein distance** W₁(μ,ν) gives the minimum total transport cost between supply and demand distributions. #### Tool 3: Adversarial Interdiction — Tri-Level Stackelberg Formulates supply chain protection as a three-player sequential game: **defender** fortifies edges → **attacker** interdicts unprotected edges → **operator** routes flow through surviving network. Benders decomposition iteratively tightens upper and lower bounds until convergence to the Stackelberg equilibrium. #### Tool 4: Supplier Survival — Competing Risks Cox Model Models supplier failure as a **competing risks** process where multiple failure modes (financial distress, operational disruption, geopolitical instability) compete to cause exit. Cause-specific hazard functions h_k(t|X) = h₀_k(t)·exp(β_k'X) allow different covariates to drive different failure modes. The **cumulative incidence function** F_k(t) properly accounts for the probability of failing from cause k before being censored by other causes. #### Tool 5: Network Resilience — Algebraic Connectivity The **Fiedler value** λ₂ (second-smallest eigenvalue of the graph Laplacian L = D - A) measures how well-connected a network is. Higher λ₂ means faster mixing, better fault tolerance, and harder to disconnect. The SDP relaxation max λ₂(L + Σ w_ij·L_ij) subject to Σ w_ij ≤ budget finds the optimal set of edges to add for maximum resilience improvement. #### Tool 6: Input-Output Impact — Leontief Inverse The **Leontief demand-driven model** x = (I-A)⁻¹·d gives total output x required to satisfy final demand d, where A is the matrix of technical coefficients (a_ij = intermediate input from sector i per unit output of sector j). The Neumann series (I-A)⁻¹ = I + A + A² + ... converges when the spectral radius ρ(A) < 1. **Forward linkages** measure a sector's importance as a supplier; **backward linkages** measure its importance as a buyer. The **Ghosh supply-side multiplier** B = (I-G)⁻¹ captures output allocation effects. #### Tool 7: Causal Disruption Paths — Fuzzy RDD **Regression discontinuity design** exploits sharp thresholds in treatment assignment to estimate causal effects. The fuzzy variant handles probabilistic treatment: nodes near the disruption boundary receive treatment with probability between 0 and 1. The **Imbens-Kalyanaraman bandwidth** h* minimizes integrated MSE of the local linear estimator, balancing bias against variance. Identified confounders are separated from genuine causal propagation channels. #### Tool 8: Multi-Scale Demand — Rao-Blackwellized Particle Filter Decomposes demand into a **hierarchical state-space model** with trend, seasonal, and noise components at multiple time scales (daily → weekly → monthly → yearly). The **Rao-Blackwellization** marginalizes the linear-Gaussian substructure analytically (Kalman filter), using particles only for the nonlinear/non-Gaussian components. This dramatically reduces variance compared to a standard particle filter. The optimal base stock level is computed from the posterior predictive distribution to achieve the target service level. ### Architecture ```` Client ──► Express (port 3018) ──► McpServer factory │ ┌─────────────┼─────────────┐ ▼ ▼ ▼ SSE transport Streamable HTTP Health endpoint │ │ ▼ ▼ getOrBuildNetwork() (5-min TTL cache) │ ┌───────────┼───────────┐ ▼ ▼ ▼ 17 Apify actors Graph build 8 analysis tools (parallel fetch) (fusion) (per-request) ```` ### Usage ```bash npm install npm run build npm start ```` Environment variables: - `APIFY_TOKEN` — Apify API token for actor execution - `PORT` — Server port (default: 3018) ### Endpoints - `GET /sse` — SSE transport for MCP - `POST /messages?sessionId=...` — SSE message handler - `POST /mcp` — Streamable HTTP transport - `GET /health` — Health check with network stats # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ryanclinton/supply-chain-digital-twin-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("ryanclinton/supply-chain-digital-twin-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call ryanclinton/supply-chain-digital-twin-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ryanclinton/supply-chain-digital-twin-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Supply Chain Digital Twin MCP", "description": "MCP intelligence server for supply chain digital twin detection and analysis.", "version": "1.0", "x-build-id": "TqeKUeQWTJT8ILHJj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~supply-chain-digital-twin-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-supply-chain-digital-twin-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ryanclinton~supply-chain-digital-twin-mcp/runs": { "post": { "operationId": "runs-sync-ryanclinton-supply-chain-digital-twin-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ryanclinton~supply-chain-digital-twin-mcp/run-sync": { "post": { "operationId": "run-sync-ryanclinton-supply-chain-digital-twin-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": {} }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```