User guide

Demand Planner X: Operator Manual

Everything you need to connect, run your first audit, and understand what DPX hands back. No dashboard required.

Quick start

Three steps, three minutes.

Connecting

Claude Code / Cursor / Windsurf / VS Code

These all read the same underlying MCP config, so one command covers all of them:

claude mcp add --transport http dpx https://dpx-mcp.vercel.app/api/mcp \
  --header "Authorization: Bearer <your-token>"

claude mcp list   # confirm it shows "Connected"

Claude Desktop

Add this to claude_desktop_config.json (Windows: %APPDATA%\Claude\claude_desktop_config.json), then restart:

{
  "mcpServers": {
    "dpx": {
      "type": "http",
      "url": "https://dpx-mcp.vercel.app/api/mcp",
      "headers": { "Authorization": "Bearer <your-token>" }
    }
  }
}

Look for the plug/tools icon in the chat bar to confirm DPX's tools loaded.

Any other MCP client

DPX is a standard MCP server over Streamable HTTP, not a Claude-only integration. Any client that speaks MCP connects the same way: point it at the URL below with your token as a Bearer header. That includes OpenAI's MCP tool connector today, and Gemini, Grok, Perplexity, or anything else as each platform adds MCP client support, since it's an open, published protocol.

Server URL:  https://dpx-mcp.vercel.app/api/mcp
Transport:   Streamable HTTP
Auth header: Authorization: Bearer <your-token>

Check your specific client's docs for the exact config syntax; this URL/header pair is all any of them need.

Edit the variables, add your own ask, copy, run.

Every playbook below is a real template: adjust the numbers to your business, fold in a custom request, and Claude runs the whole chain autonomously, calling every tool in sequence without stopping to ask permission between steps.

You'll see 4 starter workflows here, not 47. "The 47-Point Predictor" is the name of Playbook 1 specifically, named for the depth of its diagnostic, dozens of checks across ABC/XYZ classification, forecasting, lead-time variance, safety stock, and dead stock, layered with the 47-signal external demand model (economic indicators, weather, competitor pricing, social sentiment, seasonality). It's one deep workflow, not 47 separate prompts to run.

Work through this autonomously: call each tool in sequence without stopping to ask permission between steps. Only pause once, at the end, to present the results.

Execute a comprehensive supply chain diagnostic across our active SKU catalog:
1. Call run_abc_xyz_classification (lookbackDays: 90) to find our high-margin revenue drivers.
2. For all 'A' class SKUs, call run_time_series_forecast with modelType "AutoML_Ensemble", horizonDays 90.
3. Call calculate_forecast_error (metric "WMAPE", historicalPeriodDays 90) to validate confidence.
4. Call get_purchase_orders and calculate_lead_time_variance for the relevant supplier IDs.
5. Call calculate_safety_stock_drift (serviceLevelTarget 0.95) for the same SKUs.
6. Call identify_dead_stock (daysOfSupplyThreshold: 180).
Output a single "Operational Risk Matrix" table: SKU | Class | Stockout ETA | Safety Stock Drift | Trapped Capital ($) | Recommended Action.

Work through this autonomously: call each tool in sequence without stopping to ask permission between steps. Only pause once, at the end, to present the results.

Using today's safety stock drift results, for every SKU with a negative drift:
1. Calculate the deficit needed to reach 95% service level over the supplier's actual lead time.
2. Call draft_purchase_order with dryRun: true for the matching supplier.
Present line items, unit costs, and arrival windows for my review before I ask you to submit anything live.

Work through this autonomously: call each tool in sequence without stopping to ask permission between steps. Only pause once, at the end, to present the results.

Run a marketing-to-demand correlation check:
1. Call get_marketing_performance (lookbackDays: 30) across all channels.
2. Call calculate_demand_elasticity for our top 2 SKUs by revenue, independentVariable "ad_spend".
3. Call run_time_series_forecast with modelType "AutoML_Ensemble", horizonDays 30, includePromotions: true
   for the same SKUs, to see whether current spend trajectory is already priced into the forecast.
Render a Mermaid bar chart of spend vs. revenueAttributed by channel, then a markdown table with columns:
Channel | Spend | ROAS | CAC | Elasticity Read | Recommendation. Close with one paragraph: which channel
is under-invested relative to its demand lift, and which is past the point of diminishing returns.

Work through this autonomously: call each tool in sequence without stopping to ask permission between steps. Only pause once, at the end, to present the results.

Build a board-ready P&L snapshot:
1. Call calculate_pnl_impact_summary (stockoutHorizonDays: 30, daysOfSupplyThreshold: 180).
2. Call get_external_demand_signals (lookbackDays: 30) for seasonality_event and economic_indicator types,
   and flag anything landing inside the next 30 days.
Render a Mermaid pie chart of totalTrappedCapital vs. totalRevenueAtRisk, then a one-paragraph executive
summary a COO could read in 30 seconds: net exposure, the single biggest liquidation candidate, the single
biggest reorder priority, and any upcoming external signal that changes the picture. End with a markdown
table of the top 5 liquidation and top 5 reorder candidates so it can be copy-pasted straight into a
spreadsheet.

Playbooks 3 and 4 ask Claude to render a Mermaid chart (native inline rendering in Claude Desktop/claude.ai) alongside a markdown table, legible at a glance, and the table copy-pastes straight into a spreadsheet. For interactive charting and raw JSON/CSV export, use the console instead.

Reading the output

What the risk matrix columns mean.

Security

What DPX will and won't do.

Troubleshooting

Common issues.