{
  "name": "memory",
  "description": "Graph memory plugin. Provides memory-search (with optional `fields` projection for known-shape lookups), memory-write, memory-update, memory-edge (create/delete typed directed edges between pre-existing nodes), memory-update-by-name (repair surface that resolves an elementId from a (label, name, accountId) tuple when search cannot return one), memory-lookup-by-name (deterministic read surface that returns nodes by case-insensitive exact `name` match — a Person, which carries no `name`, is matched on its composed `givenName` + `familyName` display name instead — optionally constrained by labels, bypassing the memory-search ranking stack so a present entity is never missed because a diluted query ranked it below the cut), and the :Report surface (memory-report-write / memory-report-read-latest / memory-report-list) for reading from, writing to, and updating the Neo4j knowledge graph. Includes conversational memory — organic preference learning, evidence-backed recall, and transparent 'what do you know about me?' responses. Document ingestion goes through memory-ingest; the dispatched specialist produces typed-section JSON in-turn from the loaded ontology. Operator-solicited reclassify of an already-stored thread-shaped KD (today: email-thread KDs) is exposed as `kd-classify <attachmentId>` — runs a body-growth gate (proceed iff body grew ≥25% since the last classify, or first-ever classify), stages the body to a temp file under `$ACCOUNT_DIR/tmp/`, and returns a dispatch envelope for the `librarian`; on success `memory-ingest` replaces the prior :Section children and stamps `lastClassifiedAt` + `lastClassifiedBodyLength` on the parent KD. Two modes: `document` (default) for unstructured PDF/web content → :KnowledgeDocument (keyed on `attachmentId`) + :Section, and `chat` for conversation transcripts → :ConversationArchive (keyed on `conversationIdentity`) + :Section chunks; two parent labels, two writer paths chosen by which identity property is set. Conversation-archive Phase 2 splits across two read-only tools: `conversation-archive-list-chunks` pages chunk bodies for the dispatched specialist to read in-turn; `conversation-archive-derive-insights` converts the specialist's per-chunk claims into operator-facing proposals with per-kind cypher. `conversation-archive-enrich-rejection` records (or undoes) durable per-row rejections so already-triaged claims do not re-surface on re-runs. Ships five skills: `conversational-memory`, `document-ingest`, `conversation-archive` (source-agnostic transcript ingest for WhatsApp, Telegram, Signal, LinkedIn DMs, Zoom, meeting minutes, iMessage, Slack), `conversation-archive-mcp` (operator-initiated MCP-driven ingest for Granola, Otter, Circleback meetings — sibling of `conversation-archive` sharing the same writer surface and identity formula), and `conversation-archive-enrich` (per-row operator-gated insight derivation over a named archive's chunks). Ships three thinking-tool skills: `challenge` (adversarial retrieval — strongest counter-evidence from the graph for a given claim), `connect` (bridge-finding between two topics via shared graph neighbors), and `emerge` (clusters uncategorised KnowledgeDocument and Section nodes into operator-approved Concept proposals).",
  "version": "0.1.0",
  "author": {
    "name": "Rubytech LLC"
  },
  "mcpServers": {
    "memory": {
      "type": "stdio",
      "command": "node",
      "args": [
        "${CLAUDE_PLUGIN_ROOT}/lib/mcp-spawn-tee/index.js",
        "${CLAUDE_PLUGIN_ROOT}/mcp/dist/index.js"
      ],
      "env": {
        "MCP_SPAWN_TEE_NAME": "memory"
      }
    }
  }
}