Integrations Overview | Connect Pieces to Your Workflow

Discover how to integrate Pieces with your development tools via MCP Server or CLI. Learn which integration fits your workflow best.

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.