Visual Memory MCP

Name: Visual Memory MCP
Author: putervision

by putervisionversion 1.4.2i★ –iRuns on your computer

Registry nameio.github.putervision/vision-memory-mcp

Install

Overview

Description from the maker (original English)

Local visual UI cache for AI agents using perceptual hashing, CLIP, and 3D spatial grounding.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "vision-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@putervision/vision-memory-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add vision-memory -- npx -y @putervision/vision-memory-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "vision-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@putervision/vision-memory-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "vision-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@putervision/vision-memory-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account putervision, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7248 servers in this category
Published to the registry more recently than 56% of the other 7248 servers in this category
Basic checks equal to or better than 100% of the other 7248 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Visual Memory MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.4.2) reached the registry 29 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

@putervision/vision-memory-mcp

@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

⚡ Quick Start & Installation

Prerequisites: Node.js >= 18.18.0

1. Installation

# Global installation via npm

npm install -g @putervision/vision-memory-mcp

2. Workspace Initialization

Run init in your project root to scaffold database directories, .gitignore, .env, and IDE rules:

vision-memory-mcp init --yes

3. Basic MCP Client Setup

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"] } } }

Alternative Options & CLI Usage Examples

# 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

🌟 Key Highlights

👁️ Perceptual Visual Caching & Compact Slices: Sub-5ms L1/L2 dHash zero-token fast-path layout recognition and sub-1KB compact_slice export for Pentad System 1 StatePack assembly.
🎬 WebM & MP4 Video Ingestion: Digest E2E test recordings & screen captures into searchable keyframe visual states & state transition graphs.
⚡ 15 Core MCP Tools: High-coherence consolidated toolset covering perception, video memory, evidence packs, trajectory comparison, semantic retrieval, element grounding, visual SDD, snapshots, and unified context & metrics.
🔗 Dual-MCP Synergy & Immutable Evidence Packs: Deeply bridges @putervision/state-memory-mcp task DAGs with visual state memory, generating cryptographically hashable evidence packs for compliance and audit trails.
📉 Reduced Token Overhead: Caches UI states locally using dHash, local CLIP vector search, and accessibility trees to maximize vision token savings.
🚀 Sub-5ms Fast-Path Latency: Eliminates repetitive vision LLM API calls and avoids visual hallucination loops.
🎯 Element Grounding & Action Target Prediction: Maps screen elements to CSS selectors and coordinates for deterministic UI interaction.
🎨 Visual Spec-Driven Development (Visual SDD): Register design mockups or screenshots as perceptual baseline contracts to verify visual regression.
🛡️ 100% Local-First Privacy: Local LanceDB vector store, local CLIP model, zero cloud telemetry, and PII redaction guarantees.

🛠️ MCP Tool Suite

@putervision/vision-memory-mcp provides 15 production-grade consolidated MCP tools structured across 4 core visual perception & automation domains:

Perception & Semantic Search: 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).
Element Grounding & Navigation: 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).
Video Trajectories & Evidence Packs: 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).
Snapshots & Visual SDD: 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](docs/api-reference.md) and [Features & Architecture Guide](docs/features.md).

🚀 Architecture At a Glance

                     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 └──────────────┬───────────────┘

📚 Documentation Directory

Explore dedicated guides and deep dives in the docs/ directory:

Guide	Description
🏗️ [Architecture & Codebase Distillation](docs/codebase-distillation.md)	High-signal architectural overview, 4-tier pipeline, module inventory, and design decisions.
🚀 [Features & Architecture](docs/features.md)	Key features, 4-tier retrieval pipeline, element grounding, and Dual MCP Synergy.
📘 [Formal API Reference](docs/api-reference.md)	Complete specifications, parameters, and schemas for all 15 consolidated MCP tools.
🔌 [Multi-IDE Integration Guide](docs/mcp-integration.md)	Step-by-step configs for Cursor, Claude Desktop, Antigravity, Windsurf, Zed, Roo Code & Agent Rules.
💻 [CLI Commands Reference](docs/cli.md)	Full guide for all 16 CLI management, visual spec, and snapshot commands.
⚙️ [Configuration Guide](docs/configuration.md)	Complete `.env` environment variables, thresholds, and L4 vision fallback setup.
🔒 [Storage Encryption & Security](docs/STORAGE_ENCRYPTION.md)	Encryption details, local storage privacy, and PII masking guarantees.
🤝 [Contributing Guide](CONTRIBUTING.md)	Development setup, codebase structure, and submission guidelines.
🛡️ [Security Policy](SECURITY.md)	Security vulnerability reporting and privacy disclosures.
📜 [Changelog](CHANGELOG.md)	Chronological record of release features, fixes, and patch updates.

⚠️ When Not to Use This Server

While vision-memory-mcp is designed for visual frontend state caching, UI testing, and multimodal workflows, it may not be appropriate for:

Headless / Pure Backend Development: Non-visual CLI tools, database scripts, or pure backend microservices with no UI rendering. (Use state-memory-mcp standalone instead).
High-Framerate Live Video Streaming: Continuous 60 fps live video ingest without discrete keyframe or test action boundaries.
Ultra Low-Memory Embedded Environments (<512 MB RAM): Running full local CLIP neural embeddings requires ~300 MB RAM (use --skip-model-load for lightweight dHash-only perception if memory is constrained).

🧪 Testing

# Run full unit and integration test suite across all 72 test files (312 tests)

npm run test

⚖️ License & Disclaimers

Developed and maintained by PuterVision. Released under the MIT License.

Local Storage Guarantee: Provided "as is" without warranty. Screenshots, perceptual hashes, vector embeddings, and transition graphs are stored locally unencrypted at the application level in .vision-memory-mcp/. Zero telemetry or analytics data is ever transmitted.
Trademarks & Non-Affiliation: Product names (Cursor, Claude Code, Gemini, Windsurf, VS Code, Sharp, LanceDB, ONNX, HuggingFace) are property of their respective owners and used solely for compatibility identification.

Visual Memory MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

@putervision/vision-memory-mcp

⚡ Quick Start & Installation

1. Installation

2. Workspace Initialization

3. Basic MCP Client Setup

Alternative Options & CLI Usage Examples

Start server skipping heavy CLIP model downloads (air-gapped / offline mode)

Re-initialize across all registered workspace projects

Health check dependencies, sharp bindings, and git safety

Run health diagnostics & aggregate metrics across all registered projects

Inspect stored visual states and metadata in terminal ASCII table

Register baseline design mockup contract (Visual SDD)

Save visual memory checkpoint snapshot

Ingest WebM / MP4 video recording into visual state memory timeline

Open interactive force-directed visual graph viewer in browser

🌟 Key Highlights

🛠️ MCP Tool Suite

🚀 Architecture At a Glance

📚 Documentation Directory

⚠️ When Not to Use This Server

🧪 Testing

⚖️ License & Disclaimers