End-to-end guides
View All TagsIf you've ever installed an app on your computer—double-clicking a .dmg file on Mac or a .exe on Windows—you already understand .mcpb files.
Anthropic introduced the .mcpb extension (MCP Bundle) as the standard packaging format for distributing MCP servers. Think of it as the "app bundle" for AI tools: one file that contains everything needed to give your AI assistant new capabilities.
If you've used AI assistants, you've probably hit this frustration: you need to re-explain context every time you start a new conversation. That database schema you carefully described yesterday? Gone. The code snippet you asked about last week? You're pasting it in again.
MCP Resources solve this. They let you give your AI access to files and data that persist across conversations — like giving your assistant a file cabinet instead of asking them to memorize everything.
Marketing teams face a familiar challenge: exciting AI tools exist, but connecting them to existing systems feels like solving a puzzle with missing pieces. Enter Model Context Protocol (MCP) — an open standard that acts like a universal adapter, letting AI talk directly to your CRM, email platform, analytics tools, and more.
Think of MCP as the missing link between AI's potential and your marketing reality. Instead of building custom integrations or switching between disconnected tools, you can create workflows where AI actually understands your business context.
OpenAI just made it clear: if you want your MCP server to work with ChatGPT's deep research capabilities, it needs to implement two specific tools: search and fetch.
This isn't optional. It's the protocol standard that enables ChatGPT to discover and retrieve information from external data sources. And if you're using MCPBundles, you already have it.
Most APIs weren't built for AI agents. Stripe has 300+ endpoints. GitHub's API returns paginated results that change between requests. Slack rate-limits you after 20 calls per minute. None of them were designed for Claude to use directly.
We wrapped dozens of REST APIs into MCP tools and learned that a thin wrapper doesn't work. You need to rethink the interface completely—actions instead of CRUD, consolidated search instead of endless list endpoints, and server-side resilience for pagination and rate limits.
Here's what actually works.
I spent two hours staring at Claude Desktop wondering why my MCP server wouldn't connect. The config looked perfect. The server ran fine standalone. But Claude showed no tools. The problem? A single trailing comma in my JSON config that Claude silently ignored.
This is everything I wish the docs had told me upfront.
My first MCP server took three hours to get working because I made every possible mistake: no logging, broke stdio with print statements, forgot to restart Claude Desktop, and wondered why nothing worked. Your first one should take 30 minutes.
This is what actually works, with the debugging steps I wish I'd known upfront.
I watched Claude hallucinate API endpoints that didn't exist, confidently call made-up functions, and crash our systems with broken JSON. Then we implemented the Model Context Protocol (MCP), and our error rate dropped from 28% to under 3%.
This is what I wish someone had told me when I started.