How Pieces MCP and Long-Term Memory are gaining momentum in dev community

Context has always been the missing link in developer-AI interaction. Jumping from your browser to an IDE to a chat with an LLM often results in lost momentum. But what if your tools remembered everything for you, accurately, securely, and without needing you to copy-paste your brain into every chat window? 

That’s exactly what Pieces is enabling with the integration of Model Context Protocol (MCP) and Long-Term Memory (LTM).

What is MCP (Model Context Protocol) exactly?

MCP is an open standard developed by Anthropic that enables Large Language Models (LLMs) to communicate with external data sources and tools without needing custom integrations for each one.

Think of it as: An API for the AI to get context, but it is easier for the AI to use it rather than the API.

For a more detailed guide, you can check out this article written by one of colleagues.

What is LTM (Long-Term Memory)?

Pieces LTM is an advanced memory engine that:

• Captures workflow data automatically (code snippets, browser history, conversations)

• Stores everything locally for privacy and security

• Provides temporal grounding (time-based queries)

• Tracks application sources securely (where memories originated)

and if you know by now, long-term memory is basically everything you need for a smarter and more efficient AI workflow.

How they work together

For a deeper dive, check out the behind-the-scenes details of implementing the MCP server.

Let's see how LLM interact with MCP

Let's use the PiecesCLI to try to get the memories from the LTM.

bash
pip install pieces-cli
# OR
brew install pieces-cli

pieces mcp start

1. Let's initialize the connection

{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"pieces-cli-user","version":"1.0.0"}}}

Response:

{"jsonrpc":"2.0","id":1,"result":{"protocolVersion":"2024-11-05","capabilities":{"experimental":{},"tools":{"listChanged":false}},"serverInfo":{"name":"pieces-stdio-mcp","version":"0.1.0"}}}

2. Let's send a notification to the MCP server to say that we are ready to use.

{"jsonrpc":"2.0","method":"notifications/initialized","params":{}}

Notifications do not have a response; they are like sending a message to the server.

3. Let's list the tools that the MCP server has to offer. Response:

{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}

Response is long, but we can see that we have two tools:

{
    "jsonrpc": "2.0",
    "id": 2,
    "result": {
        "tools": [
            {
                "name": "ask_pieces_ltm",
                "description": "Ask Pieces a question to retrieve historical/contextual information from the user's environment.",
                "inputSchema": {
                    // Schema defined to the llm model to follow when calling the tool //
                }
            },
            {
                "name": "create_pieces_memory",
                "description": "Use this tool to capture a detailed, never-forgotten memory in Pieces. Agents and humans alike—such as Cursor, Claude, Perplexity, Goose, and ChatGPT—can leverage these memories to preserve important context or breakthroughs that occur in a project. Think of these as \"smart checkpoints\" that document your journey and ensure valuable information is always accessible for future reference. Providing thorough file and folder paths helps systems or users verify the locations on the OS and open them directly from the workstream summary.",
                "inputSchema": {
                        // Schema defined here as wee
                }
            }
        ]
    }
}

Implementation details

Available MCP tools

The Pieces MCP server exposes two primary tools:

1. ask_pieces_ltm - Memory Retrieval Tool

Purpose: Retrieve historical/contextual information from your development environment.

Parameters:

{
  question: string;        // Required: User's query
  topics?: string[];       // Topical keywords for context
  open_files?: string[];   // Currently open files in IDE
  application_sources?: string[];  // Filter by apps (Chrome, VS Code, Discord, etc.)
  chat_llm: string;        // Required: LLM being used (e.g., "gpt-4o-mini")
  related_questions?: string[];    // Supplementary questions
  connected_client?: string;       // Client name (Cursor, Claude, etc.)
}

2. create_pieces_memory-  Memory Creation Tool

Purpose: Capture detailed memories for future reference by humans and AI.

Parameters:

{
  summary_description: string;  // Required: 1-2 sentence summary
  summary: string;             // Required: Detailed markdown narrative
  project?: string;            // Absolute path to project root
  files?: string[];            // Absolute file/folder paths
  externalLinks?: string[];    // URLs to docs, repos, etc.
  connected_client?: string;   // Client creating the memory
}

Running a tool call

{
  "method": "tools/call",
  "params": {
    "name": "ask_pieces_ltm",
    "arguments": {
      "question": "what I was working on yesterday",
      "topics": [
        "cli",
        "pieces-cli",
        "pieces"
      ],
      "open_files": [],
      "application_sources": [
        "Google Chrome",
      ],
      "chat_llm": "gpt-4o-mini",
      "related_questions": [],
      "connected_client": "pieces-cli-user"
    },
  }
}

and the Pieces LTM returned a response like that

{
  "content": [
    {
      "type": "text",
      "text": "" // Here went I was working on but in details
    }
  ],
  "isError": false
}

One of the great tools that helps in debugging and checking MCP payloads in the Inspector npx @modelcontextprotocol/inspector

How to benefit from Long term memory

Pieces LTM is not just a cache or a clipboard history, it’s a fully-featured, locally stored memory system. It captures workflow data in real-time, from your IDE to your browser to your documentation. Everything is stored locally via PiecesOS, ensuring privacy, security, and full control.

But it goes further by tracking timestamps, application sources, and even grounding context temporally, so you can ask time-relative questions like, “What was I debugging yesterday?”

You don’t need to organize these memories manually. They're structured, timestamped, and retrievable using semantic queries. This opens up a new kind of productivity, where every step in your workflow is archived and accessible, whether for debugging, knowledge sharing, or just picking up where you left off.

Let memory follow you

One of the most powerful aspects of this architecture is memory continuity. You could start a task in Cursor, switch to Slack to discuss an issue, and later revisit it in VS Code, all without needing to remember every step or detail.

Your AI assistant will still have the full picture, because Pieces LTM has been silently capturing your context across time and space.

Normally, you either have to overshare context in every prompt (“I was debugging the auth module in our CLI yesterday…”) or risk vague, unhelpful results (“Fix this error pls”). But with Pieces, you can simply say: “Hey Cursor, fix the error I hit yesterday,” and the assistant has enough background to understand what you mean.

It’s a new paradigm of context portability, and it’s local, private, and seamless.

Use context smart 

MCP and LTM don’t just make your workflow smoother, they redefine the relationship between developer and AI. They’re not just tools, they’re infrastructure. With memory that never forgets and context that travels with you, the time you used to spend explaining, remembering, or retracing steps is now spent building.

Context is no longer fragile. It's durable, accessible, and, most importantly, yours.