Remember when every phone had a different charger? Mini-USB, Micro-USB, that cursed 30-pin Apple connector. You had drawers full of cables that only worked with one device. Then USB-C showed up and suddenly your laptop, phone, and headphones all used the same port.
That's what Model Context Protocol (MCP) is doing for AI apps. Except instead of charging your phone, it's letting AI tools access your company's actual knowledge without you writing another goddamn integration.
The Problem Nobody Admits
Here's what building AI features actually looked like six months ago. You want Claude to help with your codebase. Cool. You build a custom integration. Parse your files, chunk them up, figure out how to feed context to the API. Three weeks of work.
Now you want the same feature in Cursor. Different API. Different chunking strategy. Different rate limits. Another two weeks.
GitHub Copilot wants in? Guess what — you're building it again.
This is the definition of stupid work. You're not solving problems anymore. You're writing adapter code. Different APIs for the exact same goal: "Hey AI, here's my codebase, help me understand it."
Every AI tool needs access to the same stuff — your code, your docs, your database schema, your API definitions. But they all speak different languages. So you either:
Build custom integrations for each tool (insane)
Pick one tool and hope it wins (risky)
Give up on half the features you wanted (sad)
MCP kills this entire problem.
What MCP Actually Is (Without the Marketing BS)
Model Context Protocol is an open standard from Anthropic. Think of it as a contract between AI tools (clients) and knowledge sources (servers).
The client side is simple. Your AI app says "I speak MCP." That's it. Now it can talk to any MCP server without custom code.
The server side is also simple. Your codebase, database, or document system exposes an MCP interface. One time. Then any MCP-compatible tool can query it.
The magic is in the middle layer. Clients and servers never talk directly. They talk through MCP. Standard messages. Standard responses. No custom adapters needed.
The Three Things MCP Actually Gives You
1. Resources: Read-only access to stuff
Your AI tool needs to read files, documentation, database schemas. MCP lets servers expose these as resources with URIs:
Your AI tools get a menu of expert prompts instead of making users type the same thing every time.
Why This Matters More Than the LLM
Everyone obsesses over which model is smarter. GPT-4 vs Claude vs Gemini. Benchmarks. Leaderboards. Context windows.
That's the wrong thing to optimize. The bottleneck isn't the model's IQ. It's getting the right information into the model in the first place.
A genius with no context is just an expensive random text generator. A decent model with perfect context crushes a smart model with garbage context every time.
This is why Glue uses MCP for all our integrations. We index your entire codebase — every file, symbol, API route, database table. We map relationships, detect features, track code health. That's hundreds of GB of structured knowledge about your system.
Without MCP, we'd need custom plugins for Cursor, Claude Desktop, VS Code, and every new tool that launches next month. We'd spend more time on adapters than features.
With MCP, we built one server. Now any editor or AI tool that speaks MCP can query our index. Ask about features, find technical debt, understand ownership, trace dependencies. The integration code is trivial. The hard part — building the intelligence layer — we only do once.
The Real World Impact
Here's what this looks like in practice. You're working in Cursor. You need to understand how authentication works in your app.
Without MCP (the old way):
You: Explain our auth system
AI: [Generates plausible bullshit based on common patterns]
You: That's not how our code works
AI: [Generates different plausible bullshit]
You: *Opens 12 files manually*
With MCP (connected to your codebase):
You: Explain our auth system
AI: [Queries MCP server for auth-related code]
AI: [Gets actual files, database schema, API routes]
AI: Your auth uses JWT tokens stored in httpOnly cookies.
Here's the login flow: [accurate explanation with line numbers]
Database schema: users table has password_hash, mfa_enabled
Known issues: Token refresh logic in auth.service.ts has TODO
The AI isn't smarter. It just has access to reality instead of guessing.
The Part Where Everyone Screws It Up
MCP servers are easy to build badly. I've seen teams make every mistake:
Mistake 1: Exposing raw file systems
Just mounting your /src directory and calling it done. Now your AI tool is reading webpack configs and node_modules. Useless noise.
Good servers filter. They understand what's actually relevant. Code files? Yes. Test fixtures? Maybe. Lock files? No.
Mistake 2: No semantic understanding
Dumping raw code into the AI works until your codebase hits 10k files. The AI drowns in context. Can't find what matters.
You need semantic indexing. Parse symbols, extract relationships, map dependencies. When someone asks about authentication, serve the auth module, not alphabetically-first files that happen to mention "password."
This is what Glue's MCP server does. We don't just read files. We parse your entire codebase into a knowledge graph. Functions, classes, API routes, database tables, dependencies. When an AI tool queries us, we return understanding, not raw text.
Mistake 3: Static snapshots
Your code changes constantly. An MCP server built from yesterday's snapshot is already lying to you.
Good servers stay fresh. Watch for changes. Re-index automatically. When you push code at 2pm, your AI tools should know about it by 2:01pm.
Tools That Already Speak MCP
This isn't vaporware. Real tools ship with MCP support today:
Claude Desktop: Native MCP client. Connect to any server through config.
Cursor: MCP integration for codebase context.
VS Code extensions: Community plugins adding MCP support.
Custom AI tools: If you're building internal AI tooling, MCP gives you instant integration with existing servers.
The ecosystem is exploding because the protocol is simple enough that adding support takes days, not months.
Building Your First MCP Server
If you want to expose your own knowledge, the MCP SDK makes it stupidly easy:
That's the entire skeleton. You fill in the logic for your knowledge source. The protocol handles everything else.
Why This Kills the Integration Industrial Complex
Before MCP, every AI tool company had an "integrations team." Their job: build custom connectors to Notion, GitHub, Linear, Slack, Google Drive. Maintain them forever as APIs change.
This is make-work. Nobody cares about your custom GitHub integration. They care about getting GitHub data into their AI.
MCP flips this. Instead of N tools × M data sources = N×M integrations, you get:
N tools implement MCP client (one time each)
M data sources implement MCP server (one time each)
Total integrations: N + M
The math is obvious. The hard part is getting everyone to agree on a standard.
Anthropic did the heavy lifting. They built the spec, open-sourced the SDKs, and shipped native support in Claude. Now the standard exists. Tools either adopt it or explain why they're reinventing wheels.
What Happens Next
MCP is six months old. It's already in production at companies with real codebases. The ecosystem is growing faster than any AI integration standard before it.
You're going to see:
Every serious AI coding tool adding MCP support (competitive necessity)
MCP servers for every major knowledge source (databases, docs, wikis, tickets)
Companies building their internal knowledge graph as MCP servers
LLM vendors optimizing for MCP context format
The tools that resist will sound like phone manufacturers defending proprietary chargers in 2018. Technically they can do it. Nobody will care.
For us at Glue, MCP means we can focus on the hard problem — building world-class code intelligence — instead of maintaining 47 custom integrations. Our MCP server gives your AI tools access to everything we know about your codebase. Automatically. With one configuration line.
That's the future. One protocol. Infinite integrations. No more adapter hell.
The USB-C moment for AI is here. Your move is deciding which side of the standard you want to be on.