Publishing Your Dataset as a MCP Service for ChatGPT Imagine chatting with ChatGPT and asking it to analyze your company's sales data, customer information, or product inventory—without manually uploading files or copying data to ChatGPT. That's the power of Model Context Protocol (MCP) services. In this guide, I'll show you how to transform your dataset into an AI-ready service in just a few minutes, and how to share your MCP service to your clients so they can enjoy your data from ChatGPT. What is MCP and Why Should You Care? Model Context Protocol (MCP) is a new standard that allows AI like ChatGPT and Claude to securely connect to external data sources. Instead of the old way (download → upload → process), MCP enables: Real-time data access: AI assistants query your live data Secure OAuth authentication: You control who accesses what Automatic updates: Changes in your dataset are immediately available Multi-tool compatibility: Works with ChatGPT, Claude Desktop, IDEs, and more Think of it as giving ChatGPT a direct, secure connection to your data warehouse—without the hassle. Real-World Example: Publishing a Sales Dataset Let me walk you through a real example. I have a sales dataset with customer transactions, and I want ChatGPT to help me analyze it. Step 1: Upload Your Dataset First, I uploaded my CSV file with the following columns: (customer identifier) (transaction amount) (product category) (deal stage) (closing date) Initial AI Understanding Score: 42/100 😬 The score was low because column names were abbreviated and lacked context. ChatGPT would struggle to understand what means (amount? amplitude?) or what represents. Step 2: AI Enrichment (The Game Changer) I clicked "AI Enrich" and our AI analyzed the dataset. Within few seconds, it: Renamed columns for clarity: → → → → → Added rich descriptions: : "Unique identifier for each customer in the system" : "Total transaction value in US dollars" : "High-level product classification (Electronics, Apparel, Home Goods)" Identified relationships: Detected that is a primary key Recognized date fields for time-based analysis Understood numerical fields for aggregations Suggested common queries: "Show me total revenue by product category" "What is the average transaction amount?" "List customers with transactions over $1,000" New AI Understanding Score: 94/100 🎉 Step 3: Publish as MCP Service With my enriched dataset, I clicked "Publish as MCP" and got: The system automatically generated: Secure OAuth endpoint for authentication MCP-compliant API with tools for querying data Discovery metadata so ChatGPT knows what data is available Step 4: Connect to ChatGPT In ChatGPT settings, I added a custom MCP connector: Make sure you turn on Developer Mode Settings → App & Connectors → Advanced settings → Developer Mode Click add files and more → Add Source → connect more → create Paste my MCP URL: and name it as Q4 Sales Analytics Authorize with OAuth and click create → Auth with Google or Github → Q4 Sales Analytics is now connected Done! ✅ Step 5: Real ChatGPT Conversation Now for the magic. Here's an actual conversation I had with ChatGPT after connecting my service: Click the plus sign → ...More → Q4 Sales Analytics (the MCP connector name you input above) --- Me: "Can you analyze my Q4 sales data and show me which product category had the highest revenue?" ChatGPT: [Accessing Q4 Sales Analytics service...] "Based on your Q4 sales data, here are the results: Revenue by Product Category: Electronics: $487,320 (45%) Home Goods: $362,150 (33%) Apparel: $238,940 (22%) Electronics was your top performer, generating nearly half of total Q4 revenue. Would you like me to break this down further by month or customer segment?" Me: "Yes, show me Electronics revenue by month" ChatGPT: "Here's the monthly breakdown for Electronics: October: $152,400 November: $178,920 December: $156,000 November was your peak month, likely driven by Black Friday and Cyber Monday promotions. Your Electronics sales showed a 17% increase in November compared to October." --- Notice how ChatGPT: Understood the dataset structure with business context Performed aggregations and calculations Provided business insights automatically Asked intelligent follow-up questions The Power of Enrichment: Before vs. After Before Enrichment (Score: 42/100) ChatGPT tries to analyze: Me: "What was our total revenue?" ChatGPT: "I see a column called in your dataset. Is this the revenue field? Also, what currency is this in? I want to make sure I'm calculating correctly." ❌ Requires constant clarification ❌ Wastes time on basic questions ❌ Higher risk of misinterpretation After Enrichment (Score: 94/100) ChatGPT analyzes confidently: Me: "What was our total revenue?" ChatGPT: "Your total Q4 revenue was $1,088,410 USD across 3,247 transactions." ✅ Instant, accurate answer ✅ No clarification needed ✅ Confident interpretation Advanced Features You