Agent skill

Create MCP

by davekilleen in davekilleen/Dex

Build a brand-new MCP integration from scratch with a guided wizard.

MITAuto-check: notesAgent Workflows

Install Create MCP

skills CLI
$ npx skills add davekilleen/Dex --skill create-mcp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davekilleen/Dex create-mcp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davekilleen/Dex.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/create-mcp .claude/skills/create-mcp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
create-mcp
GitHub stars
493
Token cost
~5.6k tokens
SKILL.md length
1,110 words
Files
1
Skills in repo
61
Repo updated
First seen
Licence
MIT

At a glance

Build a brand-new MCP integration from scratch with a guided wizard.

  • Works in 5 steps: Understand the Connection → Design the Tools → Implementation → …
  • The user wants Dex to talk to a tool that has no existing server — build an integration for X
  • SKILL.md covers What This Command Does, Why MCP Matters: Probabilistic…, Entry Point and Phase 1: Understand the…, plus 4 more sections
  • Calls python and just; needs DEX_MCP_ONCE_TOKEN

What it does

Create MCP is an agent skill from davekilleen/Dex. 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.

Its SKILL.md is about 5.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: Your AI Chief of Staff — a personal operating system starter kit that adapts to your role. No coding required. The licence is MIT.

When your agent uses it

  • The user wants Dex to talk to a tool that has no existing server — build an integration for X
  • Dex cant connect to Y yet

Example prompts

  • “build an integration for X”
  • “Dex can”
  • “/create-mcp”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Understand the Connection
  2. Design the Tools
  3. Implementation
  4. Integration
  5. Verification

What it can do on your machine

Read from SKILL.md and the folder at commit 227f78e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • just

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DEX_MCP_ONCE_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Create MCP loads about 5.6k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 1,110 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~5.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:430
    [SERVICE]_API_KEY to your environment or .env file"
  • NoteMentions a .env fileSKILL.md:613
    d to your shell config / the vault-root `.env` file (keep `.env` owner-only: `chmod 600 .env`).

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davekilleen/Dex at commit 227f78e, republished under its MIT licence (© davekilleen). 1,110 words, ~5,633 tokens.

Download SKILL.mdSave it as .claude/skills/create-mcp/SKILL.md (or your agent's skills folder).
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`.
<!-- Generated from `.claude/skills/create-mcp/SKILL.md` by `scripts/generate-agents-skills.py`. Do not edit. -->

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:

QuestionWithout MCPWith 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.

AspectProbabilistic (AI alone)Deterministic (MCP)
Data sourceTraining patternsLive API calls
AccuracyPlausible but unreliableExact (from source)
FreshnessStale (training cutoff)Real-time
ConsistencyMay vary per querySame query = same data
GuardrailsNoneSchema 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"
- 🔗 Any API → If it has an API, we can probably connect it

**Benefits:**
- Real data, not AI guessing
- Guardrails prevent hallucination
- Live queries, not stale training data
- Structured tools with validation

**This wizard will:**
1. Help you describe what you want to connect
2. Design the integration together
3. Generate the MCP server code
4. Integrate it into Dex
5. Update all documentation so future sessions know how to use it

Ready to get started? **What would you like to connect Dex to?**

(Just describe it in plain English — e.g., "my Google Calendar", "Notion database", "company CRM")
If service hint provided:

Skip education and jump to Phase 1 with the hint as starting context.


Phase 1: Understand the Connection

Goal: Get clarity on what service and what data

Ask these questions (adapt based on what's already known):

Question 1: Service identification

What service or tool do you want to connect?

Examples:
- A specific app (Google Calendar, Notion, Salesforce)
- A type of data (my emails, my tasks, my documents)
- An API you have access to

Your answer:

Question 2: Authentication

How do you currently access this service?

1. I log in with username/password
2. I have an API key
3. It uses OAuth (Google, Microsoft login)
4. It's a local file or database
5. Something else

This helps me understand what authentication we'll need.

Question 3: Data of interest

What specific information do you want Dex to access?

Be specific about:
- What types of data (events, messages, records)
- What you'd want to read vs. write
- Any specific fields that matter most

Example: "I want to see my calendar events - title, time, attendees. Just reading, no need to create events."

Question 4: Use cases

How would you actually use this in practice?

Give me 2-3 example questions or commands you'd want to ask:
- "Show me today's meetings"
- "Find emails from [person] about [topic]"
- "What's the status of [account]?"

This shapes what tools we'll build.
After Phase 1, summarize:
**Understood. Here's what we're building:**

📦 **Service:** [service name]
🔐 **Auth method:** [auth type]
📊 **Data access:** [read/write + what data]
🎯 **Primary use cases:**
1. [use case 1]
2. [use case 2]
3. [use case 3]

Does this capture what you want? (yes / let me clarify)

Phase 2: Design the Tools

Goal: Define the specific MCP tools to build

Based on use cases, propose tool designs:

**Proposed MCP Tools**

Based on your use cases, here's what I suggest building:

| Tool Name | What It Does | Example Usage |
|-----------|--------------|---------------|
| `[tool_1]` | [description] | "[natural language example]" |
| `[tool_2]` | [description] | "[natural language example]" |
| `[tool_3]` | [description] | "[natural language example]" |

**Input parameters for each:**

### `[tool_1]`
- `param_1` (required): [description]
- `param_2` (optional): [description]

### `[tool_2]`
...

**Questions:**
1. Do these tools cover your use cases?
2. Should any tool do more or less?
3. Are there additional scenarios I missed?
Iterate until user confirms design

Keep refining based on feedback. Ask focused questions:

  • "Should [tool] also support [capability]?"
  • "What happens if [edge case]?"
  • "Do you need to filter by [field]?"
Confirm before implementation:
**Final Tool Design**

We're building an MCP server called `[server-name]` with:

| Tool | Purpose | Inputs |
|------|---------|--------|
| [tool] | [purpose] | [inputs summary] |

**Authentication:** [method + what user needs to provide]
**Configuration:** [any env vars or config needed]

Ready to build? (yes / let me adjust)

Phase 3: Implementation

Goal: Generate the MCP server code

Step 3.1: Create the server file

Generate Python code following the pattern in core/mcp/work_server.py:

python
#!/usr/bin/env python3
"""
MCP Server for [Service Name]
[Brief description of what this server does]

Tools:
- [tool_1]: [description]
- [tool_2]: [description]
"""

import os
import json
import logging
from typing import Dict, List, Optional, Any
from datetime import datetime

from mcp.server import Server, NotificationOptions
from mcp.server.models import InitializationOptions
import mcp.server.stdio
import mcp.types as types

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration from environment
[RELEVANT_CONFIG_VARS]

# ============================================================================
# SERVICE CLIENT
# ============================================================================

class [ServiceName]Client:
    """Client for interacting with [Service]"""
    
    def __init__(self):
        [initialization code]
    
    [methods for each operation]

# ============================================================================
# MCP SERVER
# ============================================================================

app = Server("[server-name]-mcp")
client = [ServiceName]Client()

@app.list_tools()
async def handle_list_tools() -> list[types.Tool]:
    """List all available tools"""
    return [
        types.Tool(
            name="[tool_name]",
            description="[tool description]",
            inputSchema={
                "type": "object",
                "properties": {
                    [property definitions]
                },
                "required": [required fields]
            }
        ),
        # ... more tools
    ]

@app.call_tool()
async def handle_call_tool(
    name: str, arguments: dict | None
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
    """Handle tool calls"""
    
    if name == "[tool_name]":
        [implementation]
        return [types.TextContent(type="text", text=json.dumps(result, indent=2))]
    
    # ... more tool handlers
    
    return [types.TextContent(type="text", text=f"Unknown tool: {name}")]

async def _main():
    """Async main entry point"""
    logger.info("Starting [Service] MCP Server")
    
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await app.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="[server-name]-mcp",
                server_version="1.0.0",
                capabilities=app.get_capabilities(
                    notification_options=NotificationOptions(),
                    experimental_capabilities={},
                ),
            ),
        )

def main():
    """Sync entry point"""
    import asyncio
    asyncio.run(_main())

if __name__ == "__main__":
    main()

Save to: core/mcp/[service_name]_server.py

Step 3.2: Update requirements

Add any new dependencies to core/mcp/requirements.txt

Step 3.3: Create launcher script (if needed)

Create core/mcp/run_[service_name].sh:

bash
#!/bin/bash
# Launch [Service] MCP server
cd "$(dirname "$0")"
source venv/bin/activate 2>/dev/null || true
python [service_name]_server.py

Make executable: chmod +x run_[service_name].sh

Tell user what was created:
**Server Created!** ✅

Files generated:
- `core/mcp/[service_name]_server.py` — The MCP server
- `core/mcp/requirements.txt` — Updated with dependencies

**Before we integrate, you'll need to:**

[Auth-specific instructions based on Phase 1]

Examples:
- For API key: "Add [SERVICE]_API_KEY to your environment or .env file"
- For OAuth: "Run the OAuth setup script: `python core/mcp/setup_[service]_auth.py`"
- For local: "No additional setup needed"

Let me know when you're ready to integrate.

Phase 4: Integration

Goal: Connect the MCP server to Dex

Step 4.1: Update CLAUDE.md

Add to the Integration Options section or create new MCP section:

markdown
### [Service Name] Integration

**Server:** `core/mcp/[service_name]_server.py`
**Purpose:** [what it does]

**Available Tools:**

| Tool | What It Does | Example |
|------|--------------|---------|
| `[tool]` | [description] | "[natural example]" |

**Configuration Required:**
- `[ENV_VAR]`: [description]

**Usage examples:**
- "[natural language request]" → uses `[tool]` tool
- "[another request]" → uses `[another_tool]` tool

Step 4.2: Add MCP Instructions (if not present)

Check if CLAUDE.md has mcp_instructions section. If not, add:

markdown
<mcp_instructions>
### [service-name]-mcp

[Description of the server and when to use it]

**Tools:**
- `[tool_name]`: [when to use and what it returns]

</mcp_instructions>

Step 4.3: Update System Guide

Add to 06-Resources/Dex_System/Dex_System_Guide.md under Integration Options:

markdown
| **[Service]** | [Brief description of capabilities] |

And add a new section if significant:

markdown
### [Service] MCP Server

Server: `core/mcp/[service_name]_server.py`

#### Available Tools

| Tool | Purpose |
|------|---------|
| `[tool]` | [description] |

#### Usage

[How to use in natural language, what to expect]

#### Configuration

| Variable | Description |
|----------|-------------|
| `[ENV_VAR]` | [what it's for] |

Phase 5: Verification

Goal: Ensure everything is properly connected

Only a custom-* entry whose command is the current sys.executable and whose args contain exactly one local .py file can use either startup check below. remote, HTTP, npm, npx, and binary entries cannot be blessed; explain that they remain structural-only.

Show full SKILL.md (522 more words)Show less
Step 5.1: Offer a one-off startup proof

Ask exactly once:

Want me to prove it starts? This runs it once from a private copy, with your user permissions, and trusts whatever it imports. The entry's configured env is ignored. (yes / no)

Only after an explicit yes, issue a fresh token bound to this one entry and pass it to the check. The checker consumes and deletes the token before validating or launching anything, so it cannot be reused. The token prevents the automatic/recurring health checks from ever launching a one-off custom server and makes each explicit approval single-use. It is not protection against another program running as you, which could run your code directly regardless:

bash
DEX_MCP_ONCE_TOKEN=$(./.venv/bin/python core/utils/smoke.py \
  --issue-mcp-once-consent custom-[server-name]) || exit 1
./.venv/bin/python core/utils/smoke.py \
  --check-mcp-once custom-[server-name] \
  --consent-token "$DEX_MCP_ONCE_TOKEN"

Show the command result honestly. A refusal or failed handshake is not permission to try another command shape or issue another token. On no or an ambiguous answer, do not issue a token and continue without running anything. The one-off check always uses a temporary vault for both cwd and VAULT_PATH; it never launches the custom code against the live vault as its working directory.

Step 5.2: Offer recurring startup checks

First inspect the eligible entry without executing it:

bash
./.venv/bin/python -m core.utils.trust_registry --inspect-mcp custom-[server-name]

Show the returned MCP name, vault-relative path, and sha256. Then ask:

Trust this exact file for recurring startup checks? This runs <vault-relative path> with your user permissions (nightly and in deep scans), and trusts whatever it imports. Dex will run only a private copy of the exact content whose sha256 is <sha256>. If the file changes, Dex refuses to run it until you bless it again.

Default: No. (yes / no)

On no or an ambiguous answer, do not create or modify System/trusted-mcps.yaml.

On an explicit yes, create the user-owned registry from its shipped template if absent, then bind the consent to the sha256 that was shown:

bash
if [ ! -e System/trusted-mcps.yaml ]; then
  cp -- System/trusted-mcps.example.yaml System/trusted-mcps.yaml
fi
./.venv/bin/python -m core.utils.trust_registry \
  --bless-mcp custom-[server-name] --expected-sha256 <sha256>

If either command refuses, report its reason and leave the entry unblessed. Never hand-add a remote, HTTP, npm, npx, binary, flagged Python (-c or -m), absolute, or .. path.

Step 5.3: Finish the verification checklist

Run verification checklist:

**Integration Complete!** 🎉

Let me verify everything is in place:

✅ Server created: `core/mcp/[service_name]_server.py`
✅ Dependencies updated: `core/mcp/requirements.txt`
✅ CLAUDE.md updated with integration docs
✅ System Guide updated

**To start using it:**

1. Install dependencies (if new ones added):

cd core/mcp && pip install -r requirements.txt


2. Add your credentials:

export [ENV_VAR]="your-value-here"

Or add to your shell config / the vault-root `.env` file (keep `.env` owner-only: `chmod 600 .env`).

3. Configure Claude Desktop to use the server:
Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "[server-name]": {
      "command": "python",
      "args": ["[full-path]/core/mcp/[service_name]_server.py"],
      "env": {
        "VAULT_PATH": "[full-path-to-dex]"
      }
    }
  }
}
  1. Test it! Try asking:
    • "[example natural language query]"

The one-off and recurring checks above are always optional.


---

## Example Walkthrough

User: /create-mcp

Claude: [Shows education intro]

User: I want to connect my Todoist

Claude: Great choice! Let me understand what you need...

[Phase 1 questions]

User: I want to see my tasks, add new ones, and mark them complete. Example: "What's on my Todoist today?" or "Add 'buy groceries' to Todoist"

Claude: Understood. Here's what we're building:

📦 Service: Todoist 🔐 Auth method: API key (Todoist provides this) 📊 Data access: Read tasks, create tasks, update task status 🎯 Primary use cases:

  1. "What's on my Todoist today/this week?"
  2. "Add [task] to Todoist"
  3. "Mark [task] as done in Todoist"

Does this capture what you want?

User: Yes, but also let me filter by project

Claude: Got it! Adding project filtering...

[Phase 2 - Tool design]

Proposed MCP Tools

Tool NameWhat It Does
list_tasksGet tasks with filters (today, date range, project)
create_taskAdd a new task to Todoist
complete_taskMark a task as done
list_projectsSee available projects

[Continues through phases...]


---

## Behaviors

### Always Do
- Start with education for new users
- Confirm understanding before building
- Generate complete, working code
- Update ALL documentation (CLAUDE.md, System Guide)
- Provide clear setup instructions
- Offer to help test

### Never Do
- Skip the design phase
- Generate partial/placeholder code
- Forget to update documentation
- Assume authentication works without explaining setup
- Create tools without clear use cases

### If stuck on technical details
- Search for the service's API documentation
- Check if an existing Python library handles auth
- Offer simpler alternatives if complexity is high

---

## Integration Checklist

After completion, verify:

- [ ] Server file created in `core/mcp/`
- [ ] Requirements.txt updated
- [ ] CLAUDE.md has integration documentation
- [ ] System Guide updated with new capabilities
- [ ] Setup instructions are clear and complete
- [ ] Example queries provided for testing

### Analytics (Required)

- [ ] Events defined for key tools (e.g., `{tool}_used`)
- [ ] Added checkbox to `System/usage_log.md` (Integrations section)
- [ ] Privacy verified: only tracks that feature was used, not content

See `.claude/reference/skill-analytics-checklist.md` for detailed guidance.
- [ ] User knows how to configure Claude to use the server

---

## Track Usage (Silent)

Call the `mark_feature_used` tool on the `dex-analytics` MCP server with `create-mcp`. This ticks the feature's box in `System/usage_log.md`, which is what `/dex-level-up` reads to recommend features that have not been tried. It writes locally and sends nothing, so it runs whether or not analytics is enabled.

**Analytics (Silent):**

Call `track_event` with event_name `mcp_created` and properties:
- (no properties — do NOT include service names)

This only fires if the user has opted into analytics. No action needed if it returns "analytics_disabled".

© davekilleen, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/create-mcp of davekilleen/Dex.

Open the folder on GitHubat commit 227f78e

Compare with similar skills

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Context Mode Output Sandboxmksglu/context-mode26k—~4.1kAutomated safety check: PassCustom licence

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    Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.

    78k GitHub starsUsed in 5 repos~1.2k tokens
    Agent WorkflowsAuto-check passed
  • MCP Integration for Plugins

    anthropics/claude-plugins-official

    Official

    Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.

    38k GitHub starsUsed in 11 repos~3.1k tokens
    Agent WorkflowsAuto-check passed
  • Crush Configuration

    charmbracelet/crush

    Explains how to configure the Crush coding agent with crushrc or crush.json, covering providers, models, LSPs, MCP servers, hooks, permissions and config precedence.

    29k GitHub stars~3.7k tokensUpdated today
    Agent WorkflowsAuto-check passed
  • Context Mode Output Sandbox

    mksglu/context-mode

    Routes large command, file, API and browser output through context-mode tools so only the needed result enters the agent's context, instead of dumping it via Bash.

    26k GitHub stars~4.1k tokensUpdated today
    Agent WorkflowsAuto-check passed
  • Migrates the compatible subset of settings and global file-based MCP servers from the Warp desktop app into Warp Agent CLI without exposing credentials or state.

    65k GitHub starsUsed in 1 repo~2.1k tokens
    Agent WorkflowsAuto-check passed

More from davekilleen/Dex

All 61 skills in this repo
  • Dspy Ruby

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    This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications.

    493 GitHub starsUsed in 1 repo~3.9k tokens
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  • Diff Adopt Profile

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    Adopt a full published Heydex profile by handle ('set me up like @davekilleen').

    493 GitHub stars~2.1k tokensUpdated today
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  • Diff Generate

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    Package one workflow — how you use Dex for a specific job — into a shareable DexDiff methodology doc.

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  • Creating Agent Skills

    davekilleen/Dex

    Expert guidance for creating, writing, and refining Claude Code Skills.

    493 GitHub starsUsed in 1 repo~1.7k tokens
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  • Feedback

    davekilleen/Dex

    Report a Dex bug to the Dex team with zero homework — Dex investigates locally, builds a privacy-safe report, shows it to you (or auto-sends if you've chosen that), and tracks the ticket until it's…

    493 GitHub stars~2.4k tokensUpdated today
    Auto-check passed
  • Dhh Rails Style

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Categories

Questions about Create MCP

What does Create MCP do?

Build a brand-new MCP integration from scratch with a guided wizard. Create MCP is an agent skill from davekilleen/Dex. Build a brand-new MCP integration from scratch with a guided wizard.

When should I use Create MCP?

Create MCP fits situations like: the user wants Dex to talk to a tool that has no existing server — build an integration for X; dex cant connect to Y yet.

How do I install Create MCP in Claude Code?

Run `npx skills add davekilleen/Dex --skill create-mcp -a claude-code`. Or copy the skill folder (.agents/skills/create-mcp in davekilleen/Dex) into .claude/skills/create-mcp in your project. Claude Code loads it when a task matches its description.

How do I install Create MCP in Codex?

Run `npx skills add davekilleen/Dex --skill create-mcp -a codex`. Or copy the skill folder (.agents/skills/create-mcp in davekilleen/Dex) into .agents/skills/create-mcp in your project. Codex loads it when a task matches its description.

Can I use Create MCP in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davekilleen/Dex --skill create-mcp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-mcp, .gemini/skills/create-mcp, .github/skills/create-mcp and .opencode/skills/create-mcp in your project.

What does Create MCP need to run?

Going by SKILL.md and its folder, Create MCP needs the command-line tools its instructions call (python and just) and credentials named DEX_MCP_ONCE_TOKEN. Our summary lists: Python 3.

Does Create MCP access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Create MCP safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Create MCP use?

Create MCP is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create MCP use?

About 5.6k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Create MCP?

Skills that share tags, products or a category with Create MCP: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Crush Configuration (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create MCP?

davekilleen (a GitHub user) maintains it in davekilleen/Dex, which has 493 GitHub stars. The repository holds 61 skills in this directory. The repository was last updated on October 8, 2026.

Source: davekilleen/Dex on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.