Pieces Long-Term Memory (MCP)

Connect OpenClaw to Pieces via MCP-only (no SSE) and use Pieces as external long-term memory. Use this when the human runs PiecesOS with LTM enabled on anoth...

Security Scan

The skill's requests and instructions match its stated purpose of connecting OpenClaw to a remote Pieces MCP server via a user-provided HTTPS tunnel; nothing in the SKILL.md asks for unrelated credentials or installs hidden code.

Purpose & Capability

The name/description describe integrating OpenClaw with Pieces MCP and the instructions focus on constructing and using an MCP URL, session handling, and using Pieces tools. The SKILL.md references MCPorter and mcp-remote (expected bridge components) but does not require unexpected credentials or unrelated system access.

Instruction Scope

Instructions are narrowly about validating a user-provided HTTPS tunnel, initializing MCP sessions, and making properly formed requests. The skill asks the agent to prompt the human to run ngrok or a similar tunnel and to paste the tunnel URL. This requires the agent to instruct the human to expose a local service to the internet (a legitimate but security-sensitive operation). The SKILL.md does not appear to instruct the agent to read unrelated host files or exfiltrate secrets.

Install Mechanism

This is an instruction-only skill with no install spec and no code files — lowest-risk install surface. It references external tools but does not attempt to download or execute code itself.

Credentials

The skill declares no required environment variables, secrets, or config paths. The runtime instructions rely on a user-supplied HTTPS URL and server-assigned session IDs only, which is proportionate to the stated purpose.

Persistence & Privilege

always is false and the skill does not request permanent or system-wide modifications. Nothing in the SKILL.md requests modifying other skills or global agent settings.

Assessment

This skill appears coherent for connecting OpenClaw to a Pieces MCP server via a tunnel. Before installing or using it: 1) Be aware it will instruct you to expose a PiecesOS MCP endpoint to the public internet via ngrok or a custom tunnel — only do this if you trust the remote machine and understand the exposure risk. Use short-lived tunnels and restrict access where possible. 2) Do not paste or share any secret tokens, SSH keys, or private URLs in public chat; the skill only needs the HTTPS tunnel URL and the server-assigned session id pattern. 3) Confirm any instructions that tell you to install or run third-party tooling (mcp-remote, MCPorter) come from trusted sources before you run them. 4) If additional parts of the SKILL.md (not included here) instruct running downloads from arbitrary URLs or ask for unrelated credentials, treat that as suspicious and stop. If you want a stricter assessment, provide the rest of SKILL.md or any lines that instruct installing binaries or running remote scripts.

Pieces Long-Term Memory MCP Skill.md

---
name: pieces-mcp
description: >
  Connect OpenClaw to Pieces via MCP-only (no SSE) and use Pieces as external
  long-term memory. Use this when the human runs PiecesOS with LTM enabled on
  another machine and exposes the MCP server via a tunnel (ngrok, custom domain,
  or any HTTPS proxy). This skill tells the agent exactly how to prompt the
  human, configure MCPorter and mcp-remote, use Pieces tools (ask_pieces_ltm,
  create_pieces_memory, *_full_text_search), and troubleshoot common failures.
---

# Pieces MCP Skill for OpenClaw (MCP-Only, No SSE)

This skill teaches you, the OpenClaw agent, how to:

1. Know **when** to use Pieces.
2. Guide the human through tunnel setup (ngrok or custom).
3. Wire OpenClaw to the Pieces MCP server using **only `/mcp`**.
4. Use Pieces tools (`ask_pieces_ltm`, `create_pieces_memory`,
   `*_full_text_search`) in your reasoning.
5. Troubleshoot when something goes wrong.

---

## 0. When to Use This Skill

Trigger this skill when:

- The human mentions **Pieces**, **PiecesOS**, **MCP**, "ask Pieces", or "long-term memory"
  in the context of OpenClaw.
- They say things like:
  - "I want OpenClaw to remember what I worked on."
  - "Hook OpenClaw up to my Pieces long-term memory."
- You see or expect a URL like:
  - `https://...ngrok-free.dev`
  - `https://username.tunnel.company.stream`
  - Any HTTPS URL they say is "the Pieces MCP tunnel".

If none of that is true, you probably don't need this skill.

---

## 1. Mental Model

Think of three components:

- **PiecesOS MCP server (remote machine)**  
  - Runs on the human's other machine.
  - Long-Term Memory (LTM) is enabled.
  - Exposed via an HTTPS tunnel:
    `/model_context_protocol/2025-03-26/mcp`.

- **OpenClaw host (this machine)**  
  - This is where you (the agent) live.
  - Uses **MCPorter** to integrate MCP servers.

- **Bridge: MCPorter + `mcp-remote`**  
  - `mcporter.json` tells MCPorter about MCP servers.
  - `mcp-remote` connects to the remote `/mcp` endpoint and exposes it to
    OpenClaw.

> **Important:** This integration is MCP-only. Do **not** use `/sse`.  
> Always use `/mcp` for both direct calls and the bridge.

---

## 2. Human Checklist (What You Must Confirm)

You must guide/confirm these three things:

1. **PiecesOS + LTM is running** on the remote machine.  
   Say something like:
   > "Make sure PiecesOS is installed and running on your other machine, and that
   > Long-Term Memory (LTM) is enabled in the Pieces Desktop App."

2. **A tunnel is available** to expose the MCP server.  
   Options include:
   - **ngrok** (most common): `ngrok http 39300`
   - **Custom tunnel** (e.g., `username.tunnel.company.stream`)
   - Any HTTPS proxy that forwards to `localhost:39300`

3. **For ngrok specifically, tell them:**

   > "On the machine running PiecesOS, please run this in a terminal:
   >
   > ```bash
   > ngrok http 39300
   > ```
   >
   > If ngrok asks for login or an authtoken, follow its instructions once.
   > When it's running, you'll see a forwarding URL like:
   >
   > `https://SOMETHING.ngrok-free.dev -> http://localhost:39300`
   >
   > Please paste that HTTPS URL here."

You cannot proceed until the human pastes a valid HTTPS tunnel URL.

---

## 3. Build and Verify the MCP URL

Once you have the base tunnel URL, construct the MCP endpoint.

If the human gave you:

```text
https://SOMETHING.ngrok-free.dev

Or a custom tunnel like:

https://name.tunnel.company.stream

Define:

MCP_URL_BASE = <the tunnel URL>
MCP_URL_MCP  = <the tunnel URL>/model_context_protocol/2025-03-26/mcp

3.1 Quick Sanity Check (Before Initialize)

Always run this GET request first to confirm the route is alive:

curl -i "MCP_URL_MCP"

Expected success response (HTTP 400):

{
  "jsonrpc": "2.0",
  "error": {
    "code": -32000,
    "message": "Bad Request: mcp-session-id header or sessionId query parameter is required"
  },
  "id": null
}

This 400 error is good — it means:

• ✅ The route exists
• ✅ The MCP server is running
• ✅ It's ready to accept properly-formed requests

If you get 404, 502, HTML, or timeout → See Section 9.1: Troubleshooting.

3.2 Understanding MCP Request Patterns (Critical!)

Before configuring MCPorter, you need to understand how MCP requests work. This section shows the exact request patterns so you don't get confused.

The Session Management Pattern

All MCP interactions follow this flow:

1. Initialize → Server assigns you a session-id
2. Use that session-id for all subsequent requests
3. Never reuse your custom session-id — always use the server-assigned one

Session ID Format

What to look for: The server-assigned session-id is typically a Unix timestamp in milliseconds — a 13-digit numeric string like 1774202062499.

Pattern: 17XXXXXXXXXXX (13 digits starting with 17 in 2025-2026)

You'll find it in the response headers, not the response body:

mcp-session-id: 1774202062499

Required Headers for ALL Requests

Content-Type: application/json
Accept: application/json, text/event-stream
mcp-session-id: <SESSION_ID>

Missing either Content-Type or Accept will cause failures!

Critical: Use File-Based JSON for curl

⚠️ Shell quoting can mangle JSON! Zsh and Bash handle quotes differently, and inline JSON with -d '{...}' is fragile. Always use file-based JSON with --data-binary @filename to avoid parsing errors.

Critical: Use String JSON-RPC IDs

⚠️ Use "id": "1" (string), not "id": 1 (integer).
The server is sensitive to id types, and string ids work consistently.

---

Step 1: Initialize Session

Create a file init.json:

{
  "jsonrpc": "2.0",
  "method": "initialize",
  "params": {
    "protocolVersion": "0.1.0",
    "capabilities": {},
    "clientInfo": {
      "name": "openclaw-agent",
      "version": "1.0"
    }
  },
  "id": "1"
}

Run:

curl -i -X POST "MCP_URL_MCP" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "mcp-session-id: init-request-001" \
  --data-binary @init.json

What happens:

• You send mcp-session-id: init-request-001 (can be any string you choose)
• Server responds with a new session-id in the response header

Look for this in the response headers:

HTTP/2 200
content-type: application/json
mcp-session-id: 1774202062499
...

Critical: Extract the mcp-session-id value from the response header (e.g., 1774202062499). This is your server-assigned session ID. Use this exact value for all subsequent requests — do NOT continue using your initial init-request-001.

Response body (200 OK):

{
  "jsonrpc": "2.0",
  "id": "1",
  "result": {
    "protocolVersion": "0.1.0",
    "capabilities": {
      "tools": {}
    },
    "serverInfo": {
      "name": "pieces",
      "version": "1.0.0"
    }
  }
}

---

Step 2: Query Long-Term Memory

Now use the server-assigned session-id for all future requests.

Create a file query.json:

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "ask_pieces_ltm",
    "arguments": {
      "question": "What did I work on today?",
      "chat_llm": "gpt-4"
    }
  },
  "id": "2"
}

Run (replacing <SERVER_SESSION_ID> with the value from Step 1):

curl -i -X POST "MCP_URL_MCP" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "mcp-session-id: <SERVER_SESSION_ID>" \
  --data-binary @query.json

Example with actual session ID:

curl -i -X POST "MCP_URL_MCP" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "mcp-session-id: 1774202062499" \
  --data-binary @query.json

Response (200 OK):

{
  "jsonrpc": "2.0",
  "id": "2",
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"summaries\":[...],\"events\":[...]}"
      }
    ]
  }
}

Important for OpenClaw Agents:

The response looks like "raw JSON garbage" to humans, but this is perfect for you!

You get:

• summaries[] — Pre-existing memory summaries with relevance scores
• events[] — Raw activity events (browser, clipboard, audio, etc.)

Parse this JSON yourself and synthesize a natural language answer for the human.

---

Step 3: Create a Memory

Create a file create_memory.json:

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "create_pieces_memory",
    "arguments": {
      "summary_description": "OpenClaw + Pieces: First Integration Session",
      "summary": "# OpenClaw + Pieces Integration\n\nThis memory documents our initial setup session.\n\n## What We Did\n- Verified Pieces MCP server via tunnel\n- Configured MCPorter with mcp-remote\n- Successfully tested ask_pieces_ltm\n\n## Key Learnings\n- Session management is critical\n- Use file-based JSON to avoid shell quoting issues\n- Use string JSON-RPC ids for consistency",
      "project": "OpenClaw + Pieces"
    }
  },
  "id": "3"
}

Run (using the same server-assigned session ID):

curl -i -X POST "MCP_URL_MCP" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "mcp-session-id: <SERVER_SESSION_ID>" \
  --data-binary @create_memory.json

Response (200 OK):

{
  "jsonrpc": "2.0",
  "id": "3",
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Memory created successfully"
      }
    ]
  }
}

---

Session ID Quick Reference

Stage	Header Value	Source
Initialize request	Any string you choose (e.g., init-001, my-session, test-123)	You create it
Initialize response	13-digit timestamp (e.g., 1774202062499)	Extract from response header
All subsequent requests	The server-assigned value from initialize	Reuse the extracted value

Common Mistakes That Will Break Things

❌ Wrong	✅ Correct
Inline JSON with -d '{...}'	File-based JSON with --data-binary @file.json
Integer id: "id": 1	String id: "id": "1"
Missing Accept header	Include Accept: application/json, text/event-stream
Using your initial session-id (init-001) for all requests	Extract server-assigned session-id from initialize response header
Looking for session-id in response body	Session-id is in the response headers
Thinking raw JSON responses are errors	Parse the JSON — it's structured data for you to synthesize!
Calling tools before initialize	Always call initialize first to get a session-id
Forgetting chat_llm parameter	Include it (even though responses may still be raw JSON)

---

4. Configure MCPorter (MCP-Only)

OpenClaw uses MCPorter to manage MCP servers. The config lives at:

~/.openclaw/workspace/config/mcporter.json

As the agent, you should:

4.1 Ensure the config directory exists

mkdir -p ~/.openclaw/workspace/config

4.2 Create or update mcporter.json

You want a pieces entry that uses mcp-remote pointed at MCP_URL_MCP:

{
  "mcpServers": {
    "pieces": {
      "command": "mcp-remote",
      "args": [
        "MCP_URL_MCP_GOES_HERE"
      ]
    }
  }
}

Example with ngrok:

{
  "mcpServers": {
    "pieces": {
      "command": "mcp-remote",
      "args": [
        "https://SOMETHING.ngrok-free.dev/model_context_protocol/2025-03-26/mcp"
      ]
    }
  }
}

Example with custom tunnel:

{
  "mcpServers": {
    "pieces": {
      "command": "mcp-remote",
      "args": [
        "https://username.tunnel.company.stream/model_context_protocol/2025-03-26/mcp"
      ]
    }
  }
}

No /sse here. This is the MCP-only configuration.

Note: mcp-remote handles the session management (initialize, session-id extraction, header management) automatically. You don't need to manually manage sessions when using MCPorter — the examples in Section 3.2 are for understanding the protocol and direct testing/troubleshooting.

4.3 Ensure mcp-remote is installed

Install mcp-remote globally:

npm install -g mcp-remote@0.1.38

You can run this yourself from the OpenClaw host's shell.

---

5. Restart the OpenClaw Gateway

After editing mcporter.json, restart the OpenClaw gateway so it picks up the Pieces MCP server:

cd ~/.openclaw/workspace
openclaw gateway restart

Wait a few seconds for it to restart.

At this point, OpenClaw should see the Pieces MCP server through MCPorter and mcp-remote → MCP_URL_MCP.

---

6. How and When to Use Pieces

Once connected, treat Pieces as your authoritative long-term memory.

6.1 When to query Pieces

Call Pieces (via ask_pieces_ltm / search tools) whenever:

The human asks about something in the past that you're unlikely to know from this session alone:

• "What did I work on today?"
• "What did I work on yesterday / last week?"
• "What have I been doing on [project] recently?"
• "Who is [person]?"

You need context for:

• Meetings:
  • "What is my next meeting?"
  • "What did we discuss in last week's standup?"
• Debugging / research:
  • "What did I try last time I debugged this issue?"
  • "What fixes have I used before for this type of error?"

6.2 Prefer ask_pieces_ltm

Use ask_pieces_ltm as your first tool for historical questions:

Send a natural-language question, e.g.:

• "What did the user work on today?"
• "Summarize the main projects the user has been working on this month."
• "Who is the user and what is their role?"

Understand the response format:

• You'll get raw JSON with summaries[] and events[] arrays
• Each entry has a combined_string field with the content
• Parse this data and synthesize a natural language answer

When you answer, say something like:

"According to your Pieces long-term memory, …"

If ask_pieces_ltm times out or is too vague, narrow the query by time and/or topic (see troubleshooting).

6.3 Use create_pieces_memory for durable summaries

Use create_pieces_memory when you want to write an important memory:

Typical use cases:

• Human profile: name, role, responsibilities, preferences, current focus.
• Decision summaries: what was decided, why, who was involved.
• Debugging recaps: symptoms, steps taken, final fix.
• Project milestones: what changed, what shipped.

Conceptual signature:

create_pieces_memory(
  summary_description: string,
  summary: string,
  project?: string,
  files?: string[],
  externalLinks?: string[],
  connected_client?: string
);

Guidelines:

• summary_description — short human-readable label.

  • Examples:
    • "Profile: <name> – Role at <company>"
    • "OpenClaw + Pieces: Initial Setup"

• summary — full markdown body:

  • Start with a # heading:
    • # Profile: <name> – Role at <company>
  • Add a few clear sections:
    • ## Role
    • ## Responsibilities
    • ## Current Focus
    • ## Notes / Preferences

• project — (optional) grouping label:

  • "OpenClaw + Pieces", "Website", "Standup Automation", etc.

• files / externalLinks / connected_client — optional context and attribution.

This makes memories both human-readable and easy for future agents to retrieve and interpret.

---

7. Direct Testing vs. MCPorter Usage

• For debugging: Use the file-based curl examples from Section 3.2 to test the MCP server directly.

• For production: Once MCPorter is configured, you don't manually manage sessions — mcp-remote does it for you. Just call the tools through OpenClaw's MCP integration normally.

---

8. Tunnel Options

This skill works with any HTTPS tunnel that forwards to localhost:39300 on the PiecesOS machine:

Tunnel Type	Example URL	Notes
ngrok	https://abc123.ngrok-free.dev	Most common; may require auth setup
Custom Pieces tunnel	https://username.tunnel.company.stream	Pre-configured by Pieces team
Any HTTPS proxy	https://your-domain.com	As long as it forwards to 39300

The MCP endpoint is always at:

<tunnel_url>/model_context_protocol/2025-03-26/mcp

---

9. Troubleshooting

9.1 MCP URL check fails (404/502/timeout)

Symptom:

curl MCP_URL_MCP returns 404/502/HTML or times out.

What to do:

Ask the human to:

1. Confirm PiecesOS is running on the remote machine.
2. Confirm the tunnel is still running (ngrok or custom).
3. Paste a fresh tunnel URL if ngrok was restarted.

Rebuild MCP_URL_MCP and test again with the quick sanity check.

9.2 Initialize returns HTTP 500 (Internal Server Error)

Symptom:

curl -i -X POST "MCP_URL_MCP" ... -d '{"jsonrpc": "2.0", ...}'
# Returns: HTTP/1.1 500 Internal Server Error

Checklist:

1. Confirm route is alive first:

   curl -i "MCP_URL_MCP"
   

   → Should return 400 with "mcp-session-id required" message → If you get 404/502, the tunnel is down (see 9.1)

2. Use file-based JSON (avoid shell quoting issues):

   # Create init.json with your JSON payload
   curl -i -X POST "MCP_URL_MCP" \
     -H "Content-Type: application/json" \
     -H "Accept: application/json, text/event-stream" \
     -H "mcp-session-id: init-request-001" \
     --data-binary @init.json
   

3. Use string JSON-RPC ids:

   {"id": "1"}  ✅ Correct
   {"id": 1}    ❌ May cause issues
   

4. Ensure BOTH headers are present:

   • Content-Type: application/json
   • Accept: application/json, text/event-stream

5. If still 500: Ask the human to restart PiecesOS and/or the tunnel, then re-test.

9.3 Tools seem missing or unresponsive

Symptoms:

• ask_pieces_ltm never seems to be called.
• Pieces tools don't show up in behavior.
• Calls time out immediately.

What to check:

1. mcporter.json:

   • Exists at ~/.openclaw/workspace/config/mcporter.json.
   • Has a pieces server using mcp-remote pointed at a valid /mcp URL.

2. mcp-remote:

   • Is installed globally:

     mcp-remote --help
     

   • If missing, install:

     npm install -g mcp-remote@0.1.38
     

3. Gateway:

   • Was restarted after editing mcporter.json:

     openclaw gateway restart
     

4. Test the MCP server directly using the file-based curl examples from Section 3.2:

   • If direct curl works but MCPorter doesn't → issue is with the bridge
   • If direct curl fails → issue is with PiecesOS or the tunnel

9.4 ask_pieces_ltm timeouts / vague answers

Symptoms:

• Pieces returns "failed to extract context" or times out.
• Answers are too generic.

What to try:

1. Narrow the question:

   • Add time:
     • "What did I work on this morning?"
     • "What did I work on this week related to [project]?"
   • Add topic:
     • "What have I done recently around MCP and ngrok?"
     • "What have I been doing on the website?"

2. If repeated timeouts continue:

   • Test with file-based curl directly (Section 3.2) to isolate whether it's an MCPorter issue
   • Confirm the MCP server is healthy with curl -i MCP_URL_MCP
   • Confirm the tunnel is still running (human-side)

9.5 Getting raw JSON instead of natural language

This is NOT a problem!

• Raw JSON responses are expected and correct for OpenClaw agents
• The summaries[] and events[] arrays contain structured data for you to parse
• You should synthesize the natural language answer yourself

If you're getting raw JSON, it means:

• ✅ The MCP server is working correctly
• ✅ Memory retrieval is working correctly
• ✅ You have structured data to work with

Simply parse the JSON and create a helpful answer for the human!

---

10. Summary of the Flow

1. Detect the need for Pieces (user mentions Pieces/MCP/long-term memory).

2. Guide the human:

   • Confirm PiecesOS + LTM is running.
   • Set up tunnel (ngrok or custom).
   • Paste the HTTPS tunnel URL.

3. Build and verify MCP_URL_MCP:

   • Quick sanity check: curl -i MCP_URL_MCP → expect 400

4. (Optional) Test directly using file-based curl examples from Section 3.2:

   • Use string ids ("id": "1")
   • Use --data-binary @file.json
   • Extract server-assigned session-id from response headers (13-digit timestamp)

5. Configure MCPorter (mcporter.json with mcp-remote → /mcp).

6. Ensure mcp-remote is installed.

7. Restart the OpenClaw gateway.

8. Use ask_pieces_ltm for reading history (parse raw JSON responses).

9. Use create_pieces_memory for writing durable summaries.

10. Apply troubleshooting steps if anything fails.

If you follow this skill, you should be able to reliably connect to and use Pieces as external long-term memory from a fresh OpenClaw instance with minimal human effort.