home / skills / willsigmon / sigstack / mcp-automation-expert

mcp-automation-expert skill

/plugins/automation/skills/mcp-automation-expert

This skill enables AI agents to orchestrate external tools via MCP, delivering faster automated workflows across data, development, and operations.

npx playbooks add skill willsigmon/sigstack --skill mcp-automation-expert

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: MCP Automation Expert
description: Model Context Protocol automation - MCP servers, agentic workflows, tool orchestration
allowed-tools: Read, Edit, Bash, WebFetch
model: sonnet
---

# MCP Automation Expert

Leverage Model Context Protocol for AI-powered automation.

## What MCP Enables
- AI agents connecting to external tools
- Standardized tool interfaces
- Cross-platform automation
- 40-60% faster agent deployment

## Top MCP Servers for Automation

### Workflow Platforms
- **n8n MCP**: Trigger workflows, orchestrate logic
- **Zapier MCP**: 5000+ app integrations
- **Make MCP**: Visual automation

### Data & Storage
- **Supabase MCP**: Database operations
- **Notion MCP**: Knowledge base access
- **Pinecone MCP**: Vector search

### Development
- **GitHub MCP**: Repo operations
- **Playwright MCP**: Browser automation
- **Filesystem MCP**: Local file access

## Configuration

### Claude Desktop
```json
{
  "mcpServers": {
    "n8n": {
      "command": "npx",
      "args": ["-y", "@n8n/mcp-server"],
      "env": {
        "N8N_API_KEY": "your-api-key"
      }
    },
    "supabase": {
      "command": "npx",
      "args": ["-y", "@supabase/mcp-server"]
    }
  }
}
```

### Claude Code
```json
// ~/.claude.json mcpServers section
{
  "github": {
    "command": "npx",
    "args": ["-y", "@modelcontextprotocol/server-github"],
    "env": { "GITHUB_TOKEN": "..." }
  }
}
```

## Agentic Workflow Pattern

```
User Request
    ↓
Claude analyzes intent
    ↓
Calls MCP tools:
  1. GitHub MCP: Create branch
  2. Filesystem MCP: Write code
  3. GitHub MCP: Push & PR
    ↓
Returns result
```

## Building Custom MCP Servers

### TypeScript Template
```typescript
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';

const server = new McpServer({
  name: 'my-automation',
  version: '1.0.0',
});

server.tool('run_workflow', {
  description: 'Execute automation workflow',
  inputSchema: {
    type: 'object',
    properties: {
      workflow_id: { type: 'string' }
    }
  }
}, async ({ workflow_id }) => {
  // Execute workflow logic
  return { success: true };
});
```

## 2026 Trends
- MCP becoming industry standard
- Gartner: 40% enterprise apps will have AI agents by end of 2026
- Tens of thousands of MCP servers available
- OAuth, resources, prompts, elicitation support

Use when: Building AI agent workflows, connecting Claude to external tools, automation orchestration

Overview

This skill guides you to automate agentic workflows using the Model Context Protocol (MCP) and a TypeScript-first server template. It focuses on connecting AI agents to external tools, standardizing tool interfaces, and speeding deployment of agent workflows. The content covers top MCP servers, configuration snippets, and a repeatable pattern for orchestrating tasks across tools.

How this skill works

The skill inspects and configures MCP servers that expose tool capabilities (workflows, storage, dev ops, browser automation) to AI agents. It shows how Claude analyzes intent then calls MCP tools in sequence — for example creating branches in GitHub, writing files to a filesystem, and pushing pull requests. It also includes a TypeScript template for authoring custom MCP servers that declare tools, input schemas, and handler logic.

When to use it

  • You need agent-driven automation that integrates multiple services (databases, apps, developer tools).
  • Building or extending MCP servers to expose custom functionality to AI agents.
  • Orchestrating repeatable workflows triggered by natural language via Claude or another agent.
  • Prototyping developer-centric automation like CI edits, browser tasks, or repo changes.
  • Standardizing tool interfaces across teams for faster agent deployment.

Best practices

  • Define clear tool input/output schemas so agents pass structured data reliably.
  • Use environment variables and secret management (API keys, tokens) for server config.
  • Compose small, focused MCP tools (single responsibility) to simplify orchestration.
  • Test agent workflows locally with stubbed MCP servers before production rollout.
  • Log tool actions and return rich results to enable agent reasoning and recovery.

Example use cases

  • Automated code edits: agent creates branch, writes code, opens PR using GitHub MCP + Filesystem MCP.
  • Cross-app workflows: trigger an n8n workflow from a natural-language request to run multi-step automation.
  • Knowledge augmentation: query Notion or Supabase via MCP to enrich agent responses with up-to-date data.
  • Search-driven actions: perform semantic retrieval with Pinecone MCP, then act on results.
  • Browser automation: run Playwright MCP to perform UI flows and report back structured outcomes.

FAQ

What languages and runtimes are supported for MCP servers?

MCP servers are language-agnostic but the provided template and examples use TypeScript/Node.js. Any runtime that implements the MCP server contract can be used.

How do I secure MCP servers in production?

Use strong API keys or OAuth, restrict network access, store secrets in a vault, and validate input schemas. Audit and log all agent-tool interactions.