Reference Reference

🔌 Extensions & Ecosystem Reference

Extensions & Ecosystem Reference

Last verified: April 2026. This page covers MCP servers, IDE integrations, skills and plugin systems, memory tools, and community resources for staying current. For a practical guide to setting these up, see Module 8.

MCP Servers (Model Context Protocol)

MCP is a protocol that lets AI agents call external tools over a standardized JSON-RPC transport. The USB-C analogy holds: one protocol, many compatible servers. Thousands of servers now exist.

MCP Servers by Category

Productivity

  • Notion: Read and write pages, query databases.
  • Google Drive / Calendar: Access files, create calendar events.

Development

  • GitHub / GitLab: Branch checkout, commit parsing, PR drafting.
  • Filesystem: Strictly scoped read/write access to specific local directories.

Data

  • PostgreSQL / SQLite: Schema inspection, SELECT queries, direct database debugging.

Communication

  • Slack: Read channels, send messages.
  • Email: Read and draft email through IMAP/SMTP integrations.

Browser

  • Puppeteer / Browser: Opens headless browsers to navigate documentation or test UIs visually.

Discovery Resources

  • mcp.so: Directory of community-submitted MCP servers, searchable by category.
  • Smithery: MCP marketplace that lets you browse, install, and manage MCP servers directly from a web UI. Handles configuration scaffolding.
  • Glama: Curated list of MCP servers with quality ratings and install guides.

IDE Context Integration

Your code editor gives the AI immediate access to your files and project structure.

Skills and Plugins

AI coding tools each have their own extension system. They differ in where rules live and what they can do.

Claude Code uses slash-command skills: reusable .md files stored in .claude/commands/ that appear as /command-name in the terminal. You can also add hooks (shell scripts that run before or after agent actions) via .claude/settings.json. Skills are project-local or global.

Cursor uses .cursorrules for always-on context rules: a file at the project root that is automatically included in every request. These are not slash commands. They set persistent instructions about code style, conventions, and tech stack. Cursor also supports extensions through the VS Code marketplace.

GitHub Copilot supports extensions that bring external data into Copilot Chat. Extensions are installed from the GitHub Marketplace and expose tools over a defined API.

Windsurf uses rules files (.windsurfrules) similar in concept to .cursorrules, applied at the workspace level to shape how the AI interprets your project.

.cursorrules and Custom Instructions

To make an agent effective, give it explicit instructions about the current repository’s style, tech stack, and conventions.

  • Usage: A .cursorrules file or a .claudecode pre-prompt file acts as the persistent system prompt.
  • Discovery: Visit cursorrules.com to find crowd-sourced rulesets for frameworks like Astro, Next.js, and Svelte.

The 2026 developer pattern is a hybrid approach: terminal-native agents and editor-integrated AI running simultaneously.

  • Cursor: A fully integrated AI-native IDE. You act as the “driver,” with the AI acting as a context-aware pair-programmer inside your editor.
  • Claude Code: An execution-first terminal agent. You act as the “architect,” telling the agent to execute large multi-file refactors autonomously in the terminal.

Developers today primarily use Cursor for rapid day-to-day coding, and pull up Claude Code (inside the Cursor terminal) when debugging complex production server issues.

Memory and Context Tooling

Long-term memory is how tools escalate from single-session chats to persisting assistants.

  1. Mem0 / Memstack: Tools designed to auto-extract user preferences and factual relationships from conversations and store them in persistent vector graphs.
  2. GraphRAG: Uses knowledge graphs instead of simple vector similarity. The practical difference: on questions that require connecting multiple related entities (e.g., “which customers had issues with product X after the Q3 price change?”), GraphRAG returns more coherent answers because the graph structure captures relationships that a flat vector index misses.

Community Hubs

These are the places where practitioners share findings, discuss new models, and post benchmarks.

  • r/LocalLLaMA (Reddit): Daily discussion of local model releases, hardware requirements, and benchmark results. Good for seeing how new models actually perform before trying them yourself.
  • Hugging Face (huggingface.co): Model hosting, dataset hosting, research papers, and community spaces. The canonical place to download open-weight models.
  • Simon Willison’s blog (simonwillison.net): Practical coverage of AI tools, MCP servers, and prompting techniques. Updated frequently with hands-on notes.
  • Matt Shumer (posts on X/Twitter): Coverage of agent architectures and multi-agent orchestration patterns, including detailed write-ups on tool calling and planning loops.

For a hands-on guide to setting up MCP servers and IDE integrations, see Module 8.