The Developer's Guide to AI Coding Infrastructure: MCP, Skills, Memories, and More

If you've been using AI coding tools for a while, you've probably noticed they're getting more capable fast. Cursor can edit multiple files at once. Claude Code can run your tests and fix failures. OpenClaw can browse the web, query databases, and deploy your code.

But how? What's the infrastructure that lets an AI model — which fundamentally just predicts text — actually interact with your file system, your database, your browser, and your deployment pipeline?

This guide breaks down the key building blocks. If you're a developer who wants to understand the ecosystem (or build on it), this is your starting point.

MCP: Model Context Protocol — The LSP of AI

MCP (Model Context Protocol) is an open standard, originally created by Anthropic, that defines how AI applications connect to external data sources and tools. Think of it as LSP (Language Server Protocol) but for AI agents.

Before LSP, every editor had to build its own integration for every language — VS Code needed a TypeScript plugin, a Python plugin, a Rust plugin, and each one was built differently. LSP standardized the interface: any editor that speaks LSP can use any language server. One protocol, universal compatibility.

MCP does the same thing for AI. Before MCP, if you wanted Claude to query your Postgres database, you'd build a custom integration. If you wanted it to read your Notion pages, that's another custom integration. Every AI tool × every service = an explosion of one-off connectors.

With MCP, you build an MCP server for each service. The server exposes a standardized set of capabilities — tools the AI can call, resources it can read, and prompts it can use. Any AI application that speaks MCP (called an MCP client) can connect to any MCP server.

How MCP Works in Practice

An MCP server is a lightweight process that communicates over stdin/stdout (for local servers) or HTTP with Server-Sent Events (for remote servers). Here's what a minimal MCP server configuration looks like:

// claude_desktop_config.json or .cursor/mcp.json
{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": {
        "DATABASE_URL": "postgresql://localhost:5432/myapp"
      }
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/dir"]
    }
  }
}

With this config, your AI agent can now query your Postgres database and read/write files in the specified directory. The agent sees these as "tools" it can call — like functions in a programming language. When it decides it needs data from your database, it calls the query tool on the postgres MCP server, gets results back as structured data, and uses that to inform its response.

The MCP ecosystem has grown fast. As of early 2026, there are official MCP servers for PostgreSQL, SQLite, GitHub, GitLab, Slack, Google Drive, Brave Search, Puppeteer (browser automation), and dozens more. Community-built servers cover everything from Jira to AWS to Stripe.

Why MCP Matters

MCP matters because it turns AI coding tools from "smart text generators" into "agents that can interact with real systems." Without MCP (or something like it), an AI can only work with what you paste into the chat window. With MCP, it can pull data from your production database, check your CI pipeline status, read your project management tool, and act on what it finds.

For teams, this means you can give your AI agent the same access a junior developer would have — read access to relevant databases, ability to create PRs, access to documentation. The agent becomes a real participant in your development workflow, not just a chatbot.

Skills and Plugins: Teaching AI How to Do Specific Tasks

Raw LLMs are generalists. They know a bit about everything but aren't experts at anything specific to your workflow. Skills (also called plugins or custom instructions) are reusable instruction sets that teach AI agents how to perform specific tasks well.

Think of a skill as a combination of:

• System prompt instructions — detailed guidance on how to approach a task
• Tool configurations — which MCP servers or tools the task requires
• Examples and templates — reference outputs so the AI knows what "good" looks like
• Constraints and guardrails — what the AI should and shouldn't do

For example, an "SEO Blog Writing" skill might include instructions on keyword placement, meta description formatting, internal linking patterns, and a template for the expected output format. A "Code Review" skill might define what to look for (security issues, performance problems, style violations), how to format feedback, and when to approve vs request changes.

OpenClaw has a skill system where each skill is defined in a SKILL.md file alongside optional tool configs. Claude Code uses CLAUDE.md files at the project root for project-specific instructions. Cursor uses .cursorrules. The implementations differ, but the concept is the same: give the AI domain-specific knowledge so it performs specialized tasks reliably.

# Example: CLAUDE.md for a Next.js project

## Project Context
- Next.js 15 app router with TypeScript
- Tailwind CSS + shadcn/ui components  
- Supabase for database and auth
- Deployed on Vercel

## Conventions
- Use server components by default
- Client components only when interactivity is needed
- All database queries go through src/lib/supabase.ts
- Use zod for all input validation
- Tests in __tests__/ directories, using vitest

## Do NOT
- Install new dependencies without asking
- Modify the auth middleware
- Change the database schema directly

Memories: How AI Agents Maintain Context Across Sessions

One of the biggest challenges with AI coding tools is context persistence. Every time you start a new chat in Cursor or a new Claude Code session, the AI starts fresh. It doesn't remember what you worked on yesterday, what decisions you made, or what you tried that didn't work.

"Memories" are the various mechanisms AI tools use to solve this problem. There are several approaches:

1. Workspace Files (MEMORY.md, AGENTS.md)

The simplest approach: store important context in files that the AI reads at the start of every session. OpenClaw uses a system of markdown files:

• MEMORY.md — long-term curated memories (decisions, preferences, lessons learned)
• AGENTS.md — behavioral instructions (how the agent should operate)
• memory/YYYY-MM-DD.md — daily session logs (raw notes from each day)

This approach is transparent and version-controlled. You can read exactly what your AI "remembers" and edit it. The downside is that it consumes context window tokens — a large MEMORY.md file means less room for actual conversation.

2. Vector Stores and Embeddings

For larger knowledge bases, tools use vector databases (like Pinecone, Chroma, or built-in embeddings) to store and retrieve relevant context. Instead of loading everything into the prompt, the AI searches for relevant memories based on the current task.

Cursor uses this for codebase indexing — your entire repo is embedded, and when you ask a question, it retrieves the most relevant files. ChatGPT's memory feature works similarly, storing conversation summaries as embeddings and retrieving relevant ones.

3. Conversation History

The most basic form: keeping previous messages in the context window. This works for short sessions but breaks down quickly. Claude's 200K token context window can hold a lot, but a full day of coding conversation can easily exceed that. Tools manage this through summarization, selective inclusion, and context windowing.

Tool Use: The Bridge Between Chat and Action

Tool use (also called "function calling") is the mechanism that lets AI models do things in the real world instead of just generating text. It's arguably the most important infrastructure component because it's what separates a chatbot from an agent.

Here's how it works at a technical level:

1. The AI model receives a list of available tools (functions with typed parameters and descriptions)
2. Based on the conversation, the model decides to call a tool instead of (or in addition to) generating text
3. The host application executes the tool call and returns the result to the model
4. The model uses the result to inform its next response or tool call

In practice, an AI coding agent might have dozens of tools available:

• File operations — read, write, edit, search files
• Shell execution — run commands, install packages, run tests
• Browser control — navigate pages, click elements, fill forms, take screenshots
• API calls — HTTP requests to external services
• Git operations — commit, branch, push, create PRs
• Database queries — via MCP servers
• Web search — find documentation, Stack Overflow answers, API references

The agent chains these tools together in loops. For example, to fix a failing test: read the test file → read the implementation → run the test to see the error → edit the implementation → run the test again → if it passes, commit. Each step is a tool call, and the AI decides which tool to use next based on the results of the previous call.

Hosting: Cloud vs Local vs Hybrid

Where your AI coding tool runs affects its capabilities, latency, privacy, and cost. There are three main models:

Cloud-Hosted (Cursor, Windsurf, Devin)

Your code context is sent to remote servers where the AI model runs. Advantages: no local GPU needed, always up-to-date models, works on any machine. Disadvantages: latency, privacy concerns (your code is on someone else's servers), requires internet, ongoing subscription cost.

Local-First (Claude Code, Codex CLI, Aider)

The tool runs on your machine and calls cloud APIs for the LLM inference, but your files never leave your machine. The tool reads your files locally and sends only the relevant context to the API. Advantages: better privacy (you control what's sent), works with your local environment (databases, services, tools). Disadvantages: you need API keys and pay per-token, setup can be more involved.

Hybrid (OpenClaw)

OpenClaw runs a daemon on your machine that has full local access (files, terminal, browser) but uses cloud LLMs (Claude) for reasoning. It can be controlled remotely via Telegram or Discord, so you can assign tasks from your phone while the agent works on your dev machine. This gives you the privacy of local execution with the convenience of remote access.

Setting Up Your First MCP Server

Let's get practical. Here's how to set up a simple MCP server that gives your AI agent access to a SQLite database.

Step 1: Install the MCP Server

# No permanent install needed — npx runs it on demand
npx -y @modelcontextprotocol/server-sqlite /path/to/your/database.db

Step 2: Configure Your AI Client

For Claude Desktop, edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "my-project-db": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sqlite",
        "/path/to/your/database.db"
      ]
    }
  }
}

For Cursor, create .cursor/mcp.json in your project root with the same structure. For Claude Code, add the server to your .mcp.json file.

Step 3: Use It

Restart your AI client. You'll now be able to ask questions like "what tables are in my database?" or "show me the last 10 orders with their customer names" and the AI will use the MCP server to query your database directly.

The Ecosystem Is Still Young

All of this infrastructure is evolving rapidly. MCP is less than two years old. Skills systems vary wildly between tools. Memory implementations are still experimental. The tooling is powerful but not yet polished.

That's actually an opportunity. Developers who understand this infrastructure layer — who can set up MCP servers, configure agent workflows, and build custom skills — are in high demand. It's the equivalent of understanding Docker and CI/CD pipelines in 2016: the early adopters who built that expertise became the DevOps engineers and platform engineers that every company now needs.

If you want to go deeper, the best resources are:

• MCP specification — modelcontextprotocol.io
• Anthropic's tool use documentation — the most detailed guide to how function calling works with Claude
• OpenClaw docs — practical examples of skills, memories, and agent configuration
• Awesome MCP Servers — community-maintained list of available MCP servers on GitHub

And if you're looking for roles where this infrastructure knowledge is valued, browse our remote vibe coding jobs. Many companies are actively hiring developers who can build and manage AI-augmented development workflows.

Keep reading

• Ai Coding Tools Comparison
• Vibe Coding Workflow