エージェントを強化
公開元自身のコマンドです。PlugMCP のエンドポイントではありません。
キーとログ付きでコピーをホスト。
npx -y @putervision/[email protected]Claude Code にはローカルコマンド用のインストールリンクがありません。下のコマンドをコピーして、ターミナルで実行してください。
claude mcp add putervision-vision-memory-mcp -- npx -y @putervision/vision-memory-mcp@1.4.2ターミナルで実行します。
{ "mcpServers": { "putervision-vision-memory-mcp": { "command": "npx", "args": [ "-y", "@putervision/[email protected]" ] } }}claude_desktop_config.json
リモート URL か、手元のマシンのコマンドか 2 つのインストール方法の違い
PlugMCP で実行
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
起動@putervision/vision-memory-mcp is a zero-infrastructure, local-first Model Context Protocol (MCP) server and CLI tool that provides AI coding assistants (such as Cursor, Claude Code, Gemini, or Copilot) with visual state caching using perceptual hashing, local CLIP embeddings, and transition graphs to eliminate repetitive vision LLM calls.
🌐 Official Documentation & Website: visionmemorymcp.com
Prerequisites: Node.js >= 18.18.0
# Global installation via npm
npm install -g @putervision/vision-memory-mcp
Run init in your project root to scaffold database directories, .gitignore, .env, and IDE rules:
vision-memory-mcp init --yes
Add to your MCP client config (e.g. .cursor/mcp.json or .vscode/mcp.json):
{
"mcpServers": {
"vision-memory-mcp": {
"command": "vision-memory-mcp",
"args": ["run"]
}
}
}
# Run stdio MCP server directly via binary (after global install)
vision-memory-mcp run
# Start server skipping heavy CLIP model downloads (air-gapped / offline mode)
vision-memory-mcp run --skip-model-load
# Re-initialize across all registered workspace projects
vision-memory-mcp init-global
# Health check dependencies, sharp bindings, and git safety
vision-memory-mcp doctor
# Run health diagnostics & aggregate metrics across all registered projects
vision-memory-mcp doctor-global
# Inspect stored visual states and metadata in terminal ASCII table
vision-memory-mcp inspect
# Register baseline design mockup contract (Visual SDD)
vision-memory-mcp spec set --name "Dashboard" --file ./dashboard-spec.png
# Save visual memory checkpoint snapshot
vision-memory-mcp snapshot save --name "v1.0-milestone"
# Ingest WebM / MP4 video recording into visual state memory timeline
vision-memory-mcp video ingest ./playwright-test.webm --category playwright_test
# Open interactive force-directed visual graph viewer in browser
vision-memory-mcp view
compact_slice export for Pentad System 1 StatePack assembly.@putervision/state-memory-mcp task DAGs with visual state memory, generating cryptographically hashable evidence packs for compliance and audit trails.@putervision/vision-memory-mcp provides 15 production-grade consolidated MCP tools structured across 4 core visual perception & automation domains:
analyze_screenshot (L1/L2 perceptual dHash & AX tree parsing, single/batch), recall_memory (text & image semantic vector search), get_session_context (aggregated cache hit metrics, recent states, and sub-1KB compact_slice export).predict_next_action (deterministic CSS selectors & bounding coordinates), record_outcome (UI action transitions & visual blockers), get_navigation_paths (BFS shortest-path planner), wait_for_visual_state (polling for target UI state).manage_video (WebM/MP4 keyframe ingestion, timeline search), compare_states (visual layout diffs & video trajectory comparison), create_evidence_pack (cryptographic audit proof linking video keyframes to state-memory DAGs), export_trajectories (multimodal fine-tuning datasets).manage_visual_spec (mockup baseline contracts & regression checks), manage_snapshot (checkpoints, export, restore), undo_visual_mutation (revert state ingestion), forget_state (privacy & PII purging).👉 For complete parameter specifications, return schemas, and example payloads, see the Formal API Reference and Features & Architecture Guide.
Incoming Screen
│
▼
┌──────────────────────────────┐
│ L1: In-Memory Cache Lookup │ ──(Hit)──▶ Return Cached Description & Grounded Elements
└──────────────┬───────────────┘
│ (Miss)
▼
┌──────────────────────────────┐
│ L2: Perceptual Hash Scan │ ──(Hit)──▶ Return Cached Description & Grounded Elements
└──────────────┬───────────────┘
│ (Miss)
▼
┌──────────────────────────────┐
│ L3: Local CLIP Vector Search │ ──(Hit)──▶ Return Semantically Close
└──────────────┬───────────────┘
│ (Miss)
▼
┌──────────────────────────────┐
│ L4: Vision LLM Fallback │ ──(Ingest)──▶ Save Redacted State to DB
└──────────────┬───────────────┘
Explore dedicated guides and deep dives in the docs/ directory:
While vision-memory-mcp is designed for visual frontend state caching, UI testing, and multimodal workflows, it may not be appropriate for:
state-memory-mcp standalone instead).--skip-model-load for lightweight dHash-only perception if memory is constrained).# Run full unit and integration test suite across all 72 test files (312 tests)
npm run test
Developed and maintained by PuterVision. Released under the MIT License.
.vision-memory-mcp/. Zero telemetry or analytics data is ever transmitted.Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.
{ "mcpServers": { "putervision-vision-memory-mcp": { "command": "npx", "args": [ "-y", "@putervision/[email protected]" ] } }}ツール
ツール一覧はまだありません。リモートサーバーは、最初のヘルスチェック後に取得されます。
よくある質問
このページで Cursor、Claude Desktop、Claude Code、VS Code 用の stdio 設定をコピーできます。PlugMCP のプロキシではなく、公開元のインストール方法を使います。
このレコードに必須の環境変数は記載されていません。
@putervision/vision Memory は stdio 経由でローカル実行されます。
最終チェックは 2026-11-10 です。 Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.