This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Give your AI coding agent the ability to search through all your local images. Privacy-first, 100% local MCP server for macOS. Uses MLX CLIP for embeddings, Daft for batch processing, and Lance for vector storage.
https://github.com/user-attachments/assets/41e167f0-bb73-4310-8c1c-4be07af21cc1
100% local - Images and embeddings never leave your machine
MCP Server - Works with Claude Code and Claude Desktop
Natural language search - Find images by describing them
Fast - 260+ images/second on Apple Silicon via MLX
Related MCP server: ContextCore
macOS with Apple Silicon (M1/M2/M3/M4)
uv (for uvx command)
Option 1: CLI
claude mcp add local-image-search -- uvx local-image-searchOption 2: Manual - add to ~/.claude.json:
{
"mcpServers": {
"local-image-search": {
"command": "uvx",
"args": ["local-image-search"]
}
}
}Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"local-image-search": {
"command": "uvx",
"args": ["local-image-search"]
}
}
}Restart Claude after setup. The first run downloads the model (~600MB) and embeds your images, which may take a few minutes. After that, it only processes new or changed files. By default, it scans your home directory (~) and skips common system folders. See Configuration Logic for details.
Scan a specific folder:
{
"args": ["local-image-search", "~/Pictures"]
}Custom excludes:
{
"args": ["local-image-search"],
"env": {
"EXCLUDE_DIRS": "Downloads,Desktop,Movies"
}
}Faster refresh:
{
"env": {
"REFRESH_INTERVAL": "30"
}
}Options | Root | Excludes |
None |
| Default excludes |
Root only | Custom root | None |
Excludes only |
| Custom excludes |
Root + Excludes | Custom root | Custom excludes |
Default excludes: Library, .Trash, .cache, Cache, node_modules, .git, .venv, venv
search_images(query, limit) - Search for images matching a text description
get_status() - Check if the service is ready (model loaded, embeddings synced)
# Clone the repo
git clone https://github.com/Eventual-Inc/local-image-search.git
cd local-image-search
# Install dependencies
uv sync
# Download and convert CLIP model (~600MB, first time only)
cd clip && uv run python convert.py && cd ..uv run python embed.py ~/Pictures # embed all images
uv run python embed.py ~/Pictures --dry-run # count and estimate time
uv run python embed.py . --no-recursive # current dir onlyEmbeddings are cached in embeddings.lance/. Re-running skips unchanged files.
Format | Extensions | Tested |
JPEG |
| Created and embedded |
PNG |
| Created and embedded |
GIF |
| Created and embedded |
WebP |
| Created and embedded |
BMP |
| Created and embedded |
TIFF |
| Created and embedded |
HEIC/HEIF |
| Real iPhone photo + converted PNG |
Corrupted or unreadable images get zero vectors (won't match searches).
Start the server (loads model once):
uv run python server.pySearch via CLI:
uv run python search.py "sunset" # list results
uv run python search.py "people" -n 10 # show 10 resultsOr via API:
curl -X POST http://127.0.0.1:8000/search \
-H "Content-Type: application/json" \
-d '{"query": "yellow mouse", "limit": 5}'uv run python simple_image_search.py # basic in-memory search (2 images)
uv run python daft_image_search.py # batch processing demolocal-image-search/
├── clip/ # MLX CLIP implementation (from ml-explore/mlx-examples)
│ ├── model.py # CLIP model architecture
│ ├── clip.py # Model loading and inference
│ ├── convert.py # HuggingFace to MLX converter
│ ├── image_processor.py # Image preprocessing
│ ├── tokenizer.py # Text tokenization
│ ├── mlx_model/ # Converted model weights (generated)
│ └── LICENSE # MIT License (Apple Inc.)
├── data/
│ └── pokemon/ # Pokemon artwork (1025 images)
├── embeddings.lance/ # Lance DB storage (generated)
├── mcp_server.py # MCP server entry point
├── server.py # FastAPI server for local API
├── search.py # CLI search tool
├── core.py # Shared utilities (EmbedImages, find_images, etc.)
├── embed.py # CLI tool to sync embeddings from a directory
├── test_embed.py # Tests for embed.py
├── simple_image_search.py # Basic in-memory search demo
├── daft_image_search.py # Daft-based batch processing demo
├── benchmark.py # Benchmark script
├── plot_benchmark.py # Generate benchmark plot
├── benchmark_results.csv # Raw benchmark data (10 runs)
├── benchmark_plot.png # Benchmark visualization
├── pyproject.toml # Project dependencies
└── uv.lock # Dependency lockfileEmbedding time for the Pokemon dataset (1025 images) on M4 Max, averaged over 10 runs.
Run benchmarks yourself:
uv run python benchmark.py # Run one iteration, appends to CSV
uv run python benchmark.py 100 # Benchmark with specific number of images
uv run python plot_benchmark.py # Generate plot from CSVMetric | Value |
Images found | 11,843 |
Scan time | ~26s |
Embed time | ~39s |
Total time | ~65s |
Embed speed | 260 img/s |
Re-run (cached) | ~31s (scan only) |
Source: PokeAPI/sprites
License: Repository is CC0 1.0 Universal
Copyright: All Pokemon images are Copyright The Pokemon Company
Source: ml-explore/mlx-examples
License: MIT License (Apple Inc.)
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