Potencia tu agente
El comando propio del publicador. No es un endpoint de PlugMCP.
Host a copy with our keys and logs.
npx -y @putervision/[email protected]Claude Code no tiene enlace de instalación para un comando local. Copia el comando de abajo y ejecútalo en una terminal.
claude mcp add putervision-vision-memory-mcp -- npx -y @putervision/vision-memory-mcp@1.4.2Ejecútalo en una terminal.
{ "mcpServers": { "putervision-vision-memory-mcp": { "command": "npx", "args": [ "-y", "@putervision/[email protected]" ] } }}claude_desktop_config.json
¿URL remota o un comando en tu máquina? En qué se diferencian las dos instalaciones
Ejecutar en PlugMCP
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.
Abrir@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]" ] } }}Herramientas
Aún no hay lista de herramientas. Los servidores remotos la reciben tras la primera comprobación de estado.
Preguntas frecuentes
Copia en esta página la configuración stdio para Cursor, Claude Desktop, Claude Code o VS Code. Usa la instalación del publicador, no un proxy de PlugMCP.
Este registro no indica ninguna variable de entorno obligatoria.
@putervision/vision Memory se ejecuta localmente por stdio.
Última comprobación: 2026-11-10. 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.