lewm-mcp

MCP server for LeWorldModel — visual anomaly detection for Claude Code agents and other MCP clients.

Uses a JEPA-style ViT encoder to compute surprise scores between frames, enabling agents to detect unexpected UI changes, video anomalies, and state mismatches.

Quick start

npx lewm-mcp

Or install globally:

npm install -g lewm-mcp
lewm-mcp

Related MCP server: markupR MCP Server

Python requirements

The model runs in a Python subprocess. Install dependencies:

pip install torch transformers Pillow numpy
# For video analysis:
pip install opencv-python

Remote inference: To run on a GPU/MPS server (e.g. 100.105.97.18), start lewm-mcp there and connect via MCP over SSH or Tailscale.

Tools

load_model

Load the ViT encoder into memory. Call once before other tools.

{ "checkpoint": "/path/to/checkpoint" }

Returns model info (param count, device, embed_dim, status).

get_model_status

Check if the model is loaded, which checkpoint, param count, device (mps/cuda/cpu).

analyze_screenshot

Encode a screenshot and compute surprise vs a previous frame.

{
  "source": "/path/to/screenshot.png",
  "previous_source": "/path/to/previous.png",
  "anomaly_threshold": 2.0
}

source accepts a file path or base64-encoded image data.

Returns: embedding, surprise_score, normalized_surprise, cosine_similarity, mse, anomaly.

compare_states

Compare expected vs actual screen states in embedding space.

{
  "expected": "/path/to/expected.png",
  "actual": "/path/to/actual.png",
  "anomaly_threshold": 0.1
}

Returns: cosine_similarity, mse, surprise_score, match, anomaly.

analyze_video

Extract frames from a video, run through ViT, return surprise timeline.

{
  "video_path": "/path/to/recording.mp4",
  "frame_sample_rate": 1,
  "sigma_threshold": 2.0,
  "top_n": 5
}

Returns: timestamps, surprise_scores, normalized_scores, anomaly_windows, top_anomalies.

run_surprise_detection

Full pipeline on a directory of screenshots or a video file.

{
  "directory": "/path/to/screenshots/",
  "threshold_multiplier": 2.0
}

Returns: timeline, exceeded_threshold, stats.

Architecture

Claude Code agent
       │
       │ MCP (stdio)
       ▼
  lewm-mcp (TypeScript)
       │
       │ stdin/stdout JSON protocol
       ▼
  model.py (Python subprocess)
       │
       ▼
  transformers ViTModel (tiny: hidden=192, layers=3, patch=16)
  runs on: mps → cuda → cpu

The Python process stays alive between tool calls — the model loads once and stays warm.

Configure in Claude Code

Add to ~/.claude/claude_desktop_config.json (or equivalent MCP config):

{
  "mcpServers": {
    "lewm-mcp": {
      "command": "npx",
      "args": ["lewm-mcp"]
    }
  }
}

Model details

Default model: tiny ViT initialized with random weights.

• hidden_size: 192

• num_hidden_layers: 3

• num_attention_heads: 3

• patch_size: 16

• image_size: 224

Pass a checkpoint path to load_model to use a fine-tuned or pretrained checkpoint (must be a transformers ViTModel checkpoint).

Environment variables

License

MIT