Pieces MCP Playbook

Use this skill when you need high-signal, reproducible ways to pull from (and optionally write back to) Pieces Long‑Term Memory (LTM) through the Pieces MCP server.

This skill is intentionally procedural: it teaches an agent (or a developer wiring an agent) how to avoid “random prompt luck” and instead follow a stable sequence: shape the request → query → validate → refine → (optionally) write back a curated memory.

What this skill assumes

• PiecesOS is installed + running and LTM is enabled (see references/PREREQS.md).
• Your MCP host is pointed at a Pieces MCP SSE URL (commonly like http://localhost:39300/model_context_protocol/2024-11-05/sse).
• Your environment provides some way to call query and write operations:
  • Either directly as MCP tools (e.g., ask_pieces_ltm, create_pieces_memory)
  • Or via wrapper tools you expose to the agent (commonly named query and write)

If you are unsure what tools exist, run the discovery script: scripts/pieces_mcp_rpc.py --list-tools (details in references/TROUBLESHOOTING.md).

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Core concepts (don’t skip)

1) Retrieval is not the answer

Pieces MCP retrieval returns context artifacts, often as JSON designed for an LLM to process, not to show to a human verbatim. Your job is to:

1. retrieve relevant artifacts
2. summarize them into a user-facing answer
3. optionally persist curated summaries back to memory

2) Start minimal, then constrain

Over‑filtering is a common cause of “no results” or poor results. The most stable pattern is:

1. Minimal query (just the question)
2. Add one constraint at a time (time window → app sources → topics)
3. Ask a follow‑up query based on what you saw returned

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Tooling contract (framework‑agnostic)

This skill is written to be compatible with two common setups.

Setup A — Direct MCP tools (recommended)

You call the tools exposed by Pieces MCP directly. Common tool names you may see:

• ask_pieces_ltm (retrieve context)
• create_pieces_memory (write a curated memory)

Note: some environments may expose only ask_pieces_ltm. Use tool discovery to confirm.

Setup B — Wrapper tools (query / write)

You provide two tools to the agent:

• query(payload) → internally calls ask_pieces_ltm
• write(payload) → internally calls create_pieces_memory (or equivalent)

If using wrappers, enforce the payload schemas described below.

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Recommended payload schemas

Query payload (minimal + optional fields)

Always supported (minimal):

• question (string)

Common optional fields (only include if supported in your environment):

• time_window (string): e.g., "today", "yesterday", "last 2 hours"
• application_sources (string[]): e.g., ["Visual Studio Code", "Google Chrome"]
• topics (string[]): keywords, entities, identifiers (repo name, ticket id)
• related_questions (string[]): follow‑ups to increase recall/coverage
• connected_client (string): the host app name (e.g., "Cursor", "Copilot")

Write payload (curated memory)

Recommended minimum:

• summary (string): one‑sentence title
• summary_description (string): 5–15 lines, structured

Strongly recommended:

• tags (string[]): stable facets like project, repo, subsystem, ticket, date
• source_hint (string): how this memory was derived (“from LTM retrieval on 2026‑01‑02”)

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The reliable workflow

Step 0 — Turn the user request into a retrieval spec

Extract:

• time scope (explicit date range beats “recently”)
• workstream (project, repo, customer, ticket)
• apps/sources (IDE, browser, terminal, chat)
• desired output (bullets, table, narrative, email, etc.)

If the user query is ambiguous, do one minimal query first, then ask a targeted clarifying question based on what comes back.

Step 1 — Minimal query

Example (wrapper tool form):

{
  "question": "What did I work on yesterday?"
}

Step 2 — Validate the retrieval (sanity checks)

Before you synthesize, verify:

• Are the returned items actually about the time range?
• Are they from the expected sources (IDE/terminal/browser)?
• Do you see the expected entities (repo name, filenames, issue id)?

If not, do one of:

• broaden the time window
• remove a filter
• add a missing entity to topics
• ask a narrower question (“Show terminal commands in Warp from yesterday afternoon”)

Step 3 — Refinement query (constrain)

Example:

{
  "question": "Summarize the debugging work I did on the caching bug yesterday afternoon.",
  "time_window": "yesterday afternoon",
  "application_sources": ["Visual Studio Code", "Warp", "Google Chrome"],
  "topics": ["cache", "redis", "TTL", "race condition", "bugfix"]
}

Step 4 — Synthesize the answer (user-facing)

Rules:

• Prefer structured output (bullets, headings) for “what did I do”
• Include concrete artifacts: filenames, commands, PR/issue ids, decisions
• Clearly mark uncertainty (“likely”, “appears to”) when the retrieval is thin

Step 5 (optional) — Write back a curated memory

Only write back if it adds future value. Good write-backs:

• daily standup summary
• incident timeline + root cause
• decision record + rationale
• “how I fixed it” runbook

Example:

{
  "summary": "Standup summary — 2026‑01‑01",
  "summary_description": "- Main focus: caching bug in API layer\n- Changes: adjusted TTL handling and added logging\n- Next: add regression test for race condition\n- Links: PR #123, ticket ABC-456",
  "tags": ["standup", "cache", "api", "ABC-456"],
  "source_hint": "Derived from LTM retrieval via Pieces MCP"
}

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Troubleshooting (fast path)

When something fails, do this in order:

1. Confirm PiecesOS is running and LTM is enabled (see references/PREREQS.md).
2. Confirm you’re using the correct MCP URL and port (see references/MCP_ENDPOINTS.md).
3. List tools via scripts/pieces_mcp_rpc.py --list-tools.
4. If retrieval fails but writing works, capture:
   • the exact tool name
   • request payload
   • raw error text and follow the checklist in references/TROUBLESHOOTING.md.

This skill includes known failure patterns reported publicly (e.g., retrieval tool failing while memory creation works) to help you design graceful degradation.

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Files in this skill

References (read when needed)

• references/PREREQS.md — enabling LTM + basic host setup
• references/MCP_ENDPOINTS.md — endpoints, message flow, and port discovery
• references/QUERY_PLAYBOOK.md — query shaping patterns + examples
• references/WRITE_PLAYBOOK.md — write-back patterns + memory templates
• references/TROUBLESHOOTING.md — failure modes + how to surface actionable errors
• references/VECTOR_SEARCH_WITH_COUCHDB.md — how a CouchDB-backed product usually handles vector search

Scripts (run when needed)

• scripts/pieces_mcp_rpc.py — list tools, call tools, and capture raw responses over SSE
• scripts/pieces_mcp_scan.py — find a working Pieces MCP port on localhost
• scripts/pieces_mcp_smoke_test.py — end-to-end “list tools → retrieve → write” test (when tools are available)