Connecting Pieces to Your Workflow

Pieces integrates with your development environment in two powerful ways: MCP Server for AI-powered IDE integrations, or CLI for terminal-based workflows. Both connect to PiecesOS to provide access to your Long-Term Memory and Timeline—the primary surfaces for captured workflow context. Pieces Drive remains available as a legacy material manager for existing saved snippets; new workflows should rely on LTM and Timeline instead.

Choosing Your Integration

MCP Server (Recommended for AI-Powered Development)

The Model Context Protocol (MCP) Server connects AI assistants like Claude, GitHub Copilot, and Cursor directly to your Pieces Long-Term Memory. MCP enables your AI coding assistants to understand your workflow context, past decisions, and project history.

Best for:

  • AI-assisted coding in IDEs (Cursor, VS Code, JetBrains)
  • Context-aware AI conversations
  • Teams using AI coding assistants
  • Visual development workflows

Supported clients:

  • Cursor, VS Code, GitHub Copilot
  • Claude Desktop, Claude Code
  • JetBrains IDEs (IntelliJ, PyCharm, WebStorm)
  • Windsurf, Cline, Continue.dev, Zed
  • And 15+ more AI-powered tools
Connect AI assistants to your Pieces Long-Term Memory with Model Context Protocol integration.

CLI (Command-Line Interface)

The Pieces CLI brings Pieces functionality to your terminal with commands for saving snippets, querying your Long-Term Memory, and accessing Conversational Search from the command line.

Best for:

  • Terminal-based development workflows
  • Command-line power users
  • Scripting and automation
  • Headless environments

Key features:

  • Conversational Search from your terminal
  • Legacy Pieces Drive commands in the CLI (save, search, share materials) for users who still rely on Drive
  • Terminal UI (TUI) for visual navigation
  • Long-Term Memory queries from CLI
Access Pieces features directly from your terminal with the command-line interface.

Decision Guide

Choose MCP Server if you:

  • Use AI coding assistants (Claude, GitHub Copilot, Cursor)
  • Work primarily in IDEs or code editors
  • Want AI to understand your workflow history
  • Need context-aware code suggestions

Choose CLI if you:

  • Prefer terminal-based workflows
  • Write scripts or automation
  • Work in headless or remote environments
  • Want quick snippet management from command line

Use Both!

Many developers use both integrations:

  • MCP for AI-assisted coding in their IDE
  • CLI for quick terminal operations and scripting

Prerequisites

Both integrations require:

  1. PiecesOS installed and running
  2. Long-Term Memory enabled (for context-aware features)
  3. Your development tools installed (IDEs, terminal, etc.)

Quick Start

MCP Server Setup

[Download and install PiecesOS](/products/core-dependencies/pieces-os/manual-installation) for your platform (macOS, Windows, Linux). Open Pieces Desktop and [enable Long-Term Memory](/products/meet-pieces/enabling-long-term-memory) to start capturing workflow context. Select your AI tool or IDE from the [MCP integrations list](/products/mcp) and follow the setup guide.

CLI Setup

[Download and install PiecesOS](/products/core-dependencies/pieces-os/manual-installation) for your platform. Follow the [CLI installation guide](/products/cli/get-started) to install the command-line tool. Run `pieces` commands in your terminal or launch the [TUI](/products/cli/tui) with `pieces tui`.

Next Steps

Ready to connect Pieces to your workflow?

Learn about Model Context Protocol and browse 20+ supported AI clients and IDEs. Explore CLI commands, Conversational Search, legacy Drive workflows, and Terminal UI features.

Need Help?

Visit Support for troubleshooting guides, FAQs, and community resources.