Imagine your AI tools could remember what you did yesterday. Not just what files you opened, but why you changed that dependency, what Judson said in your meeting, or what was discussed in your late-night Slack brainstorm. Now they can.
Say hello to the Pieces MCP Server.
It's live, it's open, and it's making your AI tools smarter by plugging them into your actual work history.
Memory for Your Favorite Dev Tools
The Pieces MCP Server connects Pieces Long-Term Memory (LTM) to any MCP-compatible client—like GitHub Copilot, Cursor, and more. That means your coding copilot now has context. Real, useful, personalized memory.
Try this prompt:
"Based on yesterday’s convo with Laurin, update my package manifest to use the latest versions."
The MCP client talks to the Pieces MCP server, grabs the memory, and updates your code using its built-in agent. No tab-switching. No digging. Just instant recall.
⚙️ What It Actually Does (TL;DR)
If you’ve been wondering “What’s this MCP buzz about?”,this explainer has you covered. But here’s the skinny:
- The Pieces MCP Server integrates directly into your dev tools.
- It delivers contextual memory to your LLM of choice (Copilot, Cursor, etc.).
- You keep using the AI tools you already love—but now with memory superpowers.
🔧 How to Get Started
You can be up and running in minutes:
- Update to the latest version of Pieces.
- Copy your local MCP server URL from the Pieces menu bar.
- Paste it into your MCP client (e.g., Copilot, Cursor).
- Ask time-aware or source-specific questions.
Boom. You’re good.
đź‘€ Want help? Grab our setup guides:
Prefer a visual walkthrough? Watch the videos here.
🛠️ Why It’s Built Different
We went with SSE (Server-Sent Events) for communication. It’s fast, lightweight, and already plays nice with PiecesOS—unlike those clunky stdio setups that need Node or extra baggage.
âś… Works out of the box with:
- GitHub Copilot
- Cursor
- Goose
- Cline
- Windsurf
❌ Not supported (yet): Claude Desktop (but there’s a workaround using lightconetech/mcp-gateway).
🧪 What Can You Ask?
Get specific. Get powerful. Try:
- “What was I working on yesterday?”
- “Refactor
utils.pyusing yesterday’s PR feedback.” - “Summarize Judson’s meeting notes and update the README.”
- “Implement the GitHub issue I was just looking at.”
If your client supports tool-calling, it’ll auto-decide when to hit up Pieces. Want to be direct? Just say:
“Ask Pieces to…”
🕵️ Under the Hood (For the Curious)
Here’s the data flow when you ask a question:
- Your MCP client passes your prompt to its LLM.
- LLM figures out it needs context → calls
ask_pieces_ltm. - The client hits the Pieces MCP Server.
- Pieces sends back relevant memories.
- Your client’s LLM builds a reply using that context.
Pieces itself doesn’t process or modify anything. It’s modular. It’s secure. And it just works.
🔍 Feature Highlights
- đź’ˇ Tool-Agnostic: Use it from any MCP-compatible client.
- 🕰️ Time-aware & Source-aware: Ask what you did in VS Code last Tuesday.
- 🤖 Agent Ready: Let LLMs apply memory-based changes directly in your code.
đź’¸ Token Costs & Tips
Heads-up: Using Pieces MCP adds some token overhead.
- Tool descriptions get included in the initial prompt.
- Memory responses add a second pass.
No worries though—just disable Pieces when you’re not using it, and fire it back up when you need that brain boost.
🚀 Ready to Try It?
Just open up your MCP client of choice, ask something with context, and watch the magic happen.
Got a cool workflow? Show us what you're building:
PiecesForDev on X, LinkedIn, Bluesky, or Discord.
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