/create-mcp

name: create-mcp
description: "Build a brand-new MCP integration from scratch with a guided wizard. Use when the user wants Dex to talk to a tool that has no existing server — 'build an integration for X', 'Dex can't connect to Y yet'. Not for installing an MCP that already exists; use `integrate-mcp`. Not for a prompt-only workflow with no external tool; use `create-skill`."

What This Command Does

**In plain English:** A guided wizard that helps you create and integrate an MCP server into Dex. No coding knowledge required - you describe what you want, we build it together.

**When to use it:**

• You want to connect Dex to an external service (calendar, email, CRM, API)
• You have data somewhere that would be useful in Dex
• You want to automate interactions with a tool you use regularly

**How to run it:**

/create-mcp                    # Starts the wizard from the beginning
/create-mcp "calendar"         # Jumps ahead with a service hint

---

Why MCP Matters: Probabilistic AI vs Deterministic Tools

The Problem with AI Alone

AI models like Claude are fundamentally **probabilistic** - they generate responses by predicting the most likely next token based on patterns in their training data. This is powerful for reasoning and language, but dangerous for facts:

| Question | Without MCP | With MCP | |----------|-------------|----------| | "What's on my calendar today?" | *"I don't have access to your calendar, but typically..."* | **Queries actual calendar, returns real events** | | "What are our top support tickets this week?" | *"Based on typical patterns, around 10-15..."* | **Queries Zendesk: "32 tickets, 12 P0, avg response time 2.3hrs"** | | "Did Sarah email about the roadmap?" | *"I can't access your email..."* | **Searches Gmail, finds 3 matching threads** |

Without MCP, AI can only:

• Guess based on general knowledge
• Hallucinate plausible-sounding but wrong answers
• Admit it doesn't have access

What MCP Actually Does

MCP (Model Context Protocol) provides **guardrails and structure** for AI interactions with external systems:

┌─────────────────────────────────────────────────────────────┐
│                    YOUR QUESTION                            │
│         "What features are customers using most?"           │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    AI REASONING                             │
│  "I need support ticket data. I have a Zendesk MCP tool    │
│   called `get_ticket_stats`. Let me call it..."            │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    MCP TOOL CALL                            │
│  Tool: get_feature_usage                                    │
│  Input: { "days": 30, "limit": 10 }                        │
│  ─────────────────────────────────────────────────────────  │
│  │ GUARDRAILS:                                          │  │
│  │ ✓ Defined input schema - can't send bad data         │  │
│  │ ✓ Authenticated connection - uses real credentials   │  │
│  │ ✓ Structured output - returns consistent format      │  │
│  │ ✓ Deterministic - same query = same results          │  │
│  └──────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    REAL DATA RESPONSE                       │
│  { "features": [                                            │
│    { "name": "Dashboard", "usage": 89% },                   │
│    { "name": "Reports", "usage": 67% },                     │
│    { "name": "Guides", "usage": 45% }                       │
│  ]}                                                         │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    AI SYNTHESIS                             │
│  "Your top 3 features by usage are Dashboard (89%),        │
│   Reports (67%), and Guides (45%). Dashboard dominates -   │
│   consider investing more there."                          │
└─────────────────────────────────────────────────────────────┘

The Key Insight

MCP doesn't make AI smarter - it gives AI **reliable ways to get real data**. The AI still does reasoning, synthesis, and explanation. But the facts come from deterministic tool calls, not probabilistic generation.

| Aspect | Probabilistic (AI alone) | Deterministic (MCP) | |--------|--------------------------|---------------------| | Data source | Training patterns | Live API calls | | Accuracy | Plausible but unreliable | Exact (from source) | | Freshness | Stale (training cutoff) | Real-time | | Consistency | May vary per query | Same query = same data | | Guardrails | None | Schema validation, auth, error handling |

---

Entry Point

If no arguments provided:

🔌 **MCP Server Creation Wizard**

MCP (Model Context Protocol) lets Dex connect to external tools and services. Instead of guessing or saying "I don't have access", AI can query real data and give you accurate answers.

**The difference MCP makes:**
- ❌ Without: "I'd estimate you have around 10-15 support tickets..."
- ✅ With: "Zendesk shows 32 tickets, 12 high priority, avg response time 2.3hrs"

**Examples of what you can build:**
- 📅 Calendar → "What meetings do I have tomorrow? Who's attending?"
- 📧 Email → "Find emails from Sarah about the Q1 roadmap"
- 💬 Slack → "What did #product-team discuss today?"
- 📊 Analytics → "Show me feature adoption for our enterprise tier"
- 🔗