# io.github.purmemo-ai/purmemo (npm · purmemo-mcp)

AI memory across Claude, ChatGPT, Gemini, and Cursor. Save, search, and recall context everywhere.

- Trust score: 74/100 (medium)
- Change this week: +24
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `purmemo-mcp`: 74/100 (this document), [markdown](https://verifymcp.io/servers/purmemo-ai-purmemo/purmemo-mcp.md), [page](https://verifymcp.io/servers/purmemo-ai-purmemo/purmemo-mcp)

## Channel facts

- Registry: `npm`
- Package: `purmemo-mcp`
- Version: `12.9.5`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 75/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (197 of 199), so this covers what we could see, not the whole tree.
  - Install-script check failed: the install command fetches or executes arbitrary code (inline_eval). An install hook runs on every machine, in CI, and on transitive installs, whether or not you ever run the server.
  - Only part of the dependency tree could be resolved (197 of 199), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 97/100
  - Source repository is publicly reachable at the declared URL.
  - Cryptographically verified build provenance (signed, bound to purmemo-ai/purmemo-mcp).
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 7 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 70/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 2659 tokens (~295/item across 9 items; 5 tools + 4 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 23/100
  - Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add purmemo-ai-purmemo -- npx -y purmemo-mcp
```

### Codex

```bash
codex mcp add purmemo-ai-purmemo -- npx -y purmemo-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "purmemo-ai-purmemo": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "purmemo-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add purmemo-ai-purmemo --command npx --arg -y --arg purmemo-mcp
```

### Hermes

```yaml
mcp_servers:
  purmemo-ai-purmemo:
    command: "npx"
    args: ["-y", "purmemo-mcp"]
```

### Other

```json
{
  "mcpServers": {
    "purmemo-ai-purmemo": {
      "command": "npx",
      "args": [
        "-y",
        "purmemo-mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-02 (score 74, +50)

- [security regression] Install scripts: unverified → fail
- [security improvement] Provenance: unverified → pass
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [security] The scripts that run when this package is installed changed: postinstall
- [security] The attested source repository moved: purmemo-ai/purmemo-mcp
- [functional improvement] License: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional improvement] Stability: unverified → 0.20
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] Schema quality: unverified → good
- [functional improvement] MCP protocol: unverified → pass
- [functional] Licence: MIT

### 2026-08-01 (score 24, +19)

- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] Schema quality: unverified → 100

### 2026-07-31 (score 5, −27)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 32, −18)

- [security regression] Malware scan: pass → unverified

### 2026-07-27 (score 50)

First indexed and scored.

## MCP tools (5)

### `save_conversation` (~820 tokens)

Save complete conversations as living documents. REQUIRED: Send COMPLETE conversation in 'conversationContent' parameter (minimum 100 chars, should be thousands). Include EVERY message verbatim - NO summaries or partial content.

    Intelligently tracks context, extracts project details, and maintains a single memory per conversation topic.

    LIVING DOCUMENT + INTELLIGENT PROJECT TRACKING:
    - Each conversation becomes a living document that grows over time
    - Automatically extracts project context (name, component, feature being discussed)
    - Detects work iteration and status (planning/in_progress/completed/blocked)
    - Generates smart titles like "Purmemo - Timeline View - Implementation" (no more timestamp titles!)
    - Tracks technologies, tools used, and identifies relationships/dependencies
    - Works like Chrome extension: intelligent memory that grows with each save

    How memory updating works:
    - Conversation ID auto-generated from title (e.g., "MCP Tools" → "mcp-tools")
    - Same title → UPDATES existing memory (not create duplicate)
    - "Save progress" → Updates most recent memory for current project context
    - Explicit conversationId → Always updates that specific memory
    - Example: Saving "Project X Planning" three times = ONE memory updated three times
    - To force new memory: Change title or use different conversationId

    SERVER AUTO-CHUNKING:
    - Large conversations (>15K chars) automatically split into linked chunks
    - Small conversations (<15K chars) saved directly as single memory
    - You always send complete content - server handles chunking intelligently
    - All chunks linked together for seamless retrieval

    EXAMPLES:
    User: "Save progress" (working on Purmemo timeline feature)
    → System auto-generates: "Purmemo - Timeline View - Implementation"
    → Updates existing memory if this title was used before

    User: "Save this conversation" (discussing React hooks implementation)
    → Syste…

Input parameters:

- `conversationContent` (string, required): COMPLETE conversation transcript - minimum 500 characters expected. Include EVERYTHING discussed.
- `conversationId` (string): Optional unique identifier for living document pattern. If provided and memory exists with this conversationId, UPDATES that memory instead of creating new one. Use for maintaining single memory per…
- `priority` (string): Priority level for this memory
- `tags` (array): Tags for categorization
- `title` (string): Title for this conversation memory

### `recall_memories` (~764 tokens)

Search and retrieve saved memories with intelligent semantic ranking.

🎯 BASIC SEARCH:
  recall_memories(query="authentication")
  → Returns all memories about authentication, ranked by semantic relevance

🔍 FILTERED SEARCH (Phase 2 Knowledge Graph Intelligence):
  Use filters when you need PRECISION over semantic similarity:

  ✓ entity="name" - Find memories mentioning specific people/projects/technologies
    Example: entity="purmemo" → Only memories discussing purmemo

  ✓ has_observations=true - Find substantial, fact-dense conversations
    Example: has_observations=true → Only high-quality technical discussions

  ✓ initiative="project" - Scope to specific initiatives/goals
    Example: initiative="Q1 OKRs" → Only Q1-related memories

  ✓ intent="type" - Filter by conversation purpose
    Options: decision, learning, question, blocker
    Example: intent="blocker" → Only conversations about blockers

💡 WHEN TO FILTER:
  \- Use entity when user asks about specific person/project by name
  \- Use has_observations for "detailed" or "substantial" requests
  \- Use initiative/stakeholder for project-specific searches
  \- Use intent when user asks for decisions, learnings, or blockers

📝 COMBINED EXAMPLES:
  recall_memories(query="auth", entity="purmemo", has_observations=true)
  → Find detailed technical discussions about purmemo authentication

  recall_memories(query="blockers", intent="blocker", stakeholder="Engineering")
  → Find engineering team blockers

Input parameters:

- `contentPreview` (boolean): Include content preview in results
- `deadline` (string): Filter by deadline date from conversation context (YYYY-MM-DD format). Use when user asks about time-sensitive memories or specific deadlines. Example: deadline="2025-03-31" finds memories with March…
- `entity` (string): Filter by entity name (people, projects, technologies). Use when user asks about a specific person, project, or technology by name. Example: entity="Alice" finds only memories mentioning Alice. More…
- `has_observations` (boolean): Filter by conversation quality based on extracted observations (atomic facts). Set to true to find substantial, structured conversations with extracted knowledge (high-quality technical discussions,…
- `includeChunked` (boolean): Include chunked/multi-part conversations in results
- `initiative` (string): Filter by initiative/project name from conversation context. Use when user scopes search to specific project or goal. Example: initiative="Q1 OKRs" finds only Q1-related memories. Supports partial ma…
- `intent` (string): Filter by conversation intent/purpose. Options: "decision" (decisions made), "learning" (knowledge gained), "question" (open questions), "blocker" (obstacles/issues). Use when user asks specifically…
- `limit` (integer): Maximum number of memories to return
- `query` (string, required): Search query - can be keywords, topics, or specific content
- `stakeholder` (string): Filter by stakeholder (person or team) from conversation context. Use when user asks about specific person's or team's involvement. Example: stakeholder="Engineering Team" finds memories where Engine…

### `get_memory_details` (~59 tokens)

Get complete details of a specific memory, including all linked parts if chunked

Input parameters:

- `includeLinkedParts` (boolean): Include all linked parts if this is a chunked memory
- `memoryId` (string, required): ID of the memory to retrieve

### `discover_related_conversations` (~275 tokens)

CROSS-PLATFORM DISCOVERY: Find related conversations across ALL AI platforms.

    Uses Purmemo's semantic clustering to automatically discover conversations about similar topics,
    regardless of which AI platform was used (ChatGPT, Claude Desktop, Gemini, etc).

    WHAT THIS DOES:
    - Searches for memories matching your query
    - Uses AI-organized semantic clusters to find related conversations
    - Groups results by topic cluster with platform indicators
    - Shows conversations you may have forgotten about on other platforms

    EXAMPLES:
    User: "Show me all conversations about the marketing project"
    → Finds conversations across ChatGPT, Claude, Gemini automatically

    User: "What have I discussed about licensing requirements?"
    → Discovers related discussions from all platforms, grouped by semantic similarity

    User: "Find everything about React hooks"
    → Returns conversations from any platform where you discussed React hooks

    RESPONSE FORMAT:
    Shows memories grouped by semantic cluster with platform badges (ChatGPT, Claude, Gemini)
    Each cluster represents conversations about similar topics across all platforms

Input parameters:

- `limit` (integer): Maximum number of initial search results (will find related for each)
- `query` (string, required): Natural language query for discovering related conversations across platforms
- `relatedPerMemory` (integer): Maximum related conversations to find per result

### `get_user_context` (~271 tokens)

Get the current user's cognitive identity and active session context.

Call this at the START of a conversation to understand who you're talking to —
their role, expertise, current project, and recent memory themes.

This is the core of Purmemo's identity layer: once set in the dashboard,
your identity travels silently to every AI session so you're never explaining
yourself from scratch again.

WHAT IT RETURNS:
\- identity: role, expertise areas, primary domain, work style, preferred tools
\- current_session: what the user is working on right now (project, focus)
\- memory_summary: 2-3 sentence synthesis of the user's most recent memory themes

WHEN TO CALL:
\- At the start of every new session (add to Claude system prompt)
\- When user says "load my context" or "what do you know about me?"
\- Before making recommendations that depend on knowing the user's background

EXAMPLE USAGE:
→ User starts new Claude session
→ Claude calls get_user_context automatically
→ Response: { role: "founder", expertise: ["product", "fullstack"],
              project: "purmemo", focus: "identity layer",
              memory_summary: "Chris has been building Purmemo's..." }
→ Claude responds with full context already loaded — no re-explaining needed

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/purmemo-ai-purmemo/purmemo-mcp#diagnostics

## Score history

- 2026-08-03: 74
- 2026-08-02: 74
- 2026-08-01: 24
- 2026-07-31: 5
- 2026-07-30: 32
- 2026-07-28: 50
- 2026-07-27: 50

## Links

- npm package: https://www.npmjs.com/package/purmemo-mcp
- Socket report: https://socket.dev/npm/package/purmemo-mcp
- Repository: https://github.com/purmemo-ai/purmemo-mcp
- Changelog RSS feed: https://verifymcp.io/servers/purmemo-ai-purmemo/purmemo-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/purmemo-ai-purmemo/purmemo-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/purmemo-ai-purmemo/purmemo-mcp