StockSpark MCP Server

StockSpark MCP Discount Workflow - Simple Text Version ===================================================== USER STORY: "Apply 5% discount to all vehicles with 60+ days in stock and no leads" WORKFLOW DIAGRAM: ----------------- [User Request in Claude Desktop] | v [Claude AI Agent Plans Workflow] | v [1. get_performance_analytics (period: 30d)] | v [StockSpark API Returns vehicle metrics] | v [AI filters: daysInStock > 60] --> 23 vehicles found | v [2. get_vehicle_leads for each vehicle] | v [StockSpark API Returns lead counts] | v [AI filters: leadCount = 0] --> 15 vehicles with no leads | v [3. update_vehicle (price × 0.95)] | v [StockSpark API Updates prices] | v [✓ 15 vehicles updated with 5% discount] ARCHITECTURE VIEW: ------------------ User --> Claude Desktop --> MCP Server (41 Tools) --> StockSpark API | |-- get_performance_analytics |-- get_vehicle_leads |-- update_vehicle STEP-BY-STEP PROCESS: --------------------- Step 1: Find Old Vehicles ------------------------- Tool: get_performance_analytics Result: 23 vehicles with 60+ days in stock Step 2: Check Leads ------------------- Tool: get_vehicle_leads (for each vehicle) Result: 15 vehicles have zero leads Step 3: Apply Discount ---------------------- Tool: update_vehicle (Apply 5% discount) Result: ✓ Prices updated KEY BENEFITS: ------------- MCP Benefits: ├── Natural Language │ ├── "Apply discount to old stock" │ └── No coding required ├── AI Orchestration │ ├── Chains multiple tools │ └── Handles complex logic └── Business Value ├── Automated pricing ├── Lead-based decisions └── 60+ day inventory management SUMMARY: -------- The MCP server enables natural language commands to execute complex business workflows by orchestrating multiple API calls through AI agents. This example shows how a simple request triggers a multi-step process involving analytics, lead checking, and price updates.