Darktable MCP Server

A Model Context Protocol (MCP) server that exposes darktable operations to MCP clients (Claude Desktop, Claude Code, etc.). The AI lives in the client; this server drives darktable.

Tools

Library operations (require darktable-mcp install-plugin and an open darktable session):

• view_photos(filter?, rating_min?, limit?) — Browse the library by filename substring and minimum rating. Returns id, filename, absolute file path, and rating per match — the path drops straight into export_images's photo_ids.

• rate_photos(photo_ids, rating) — Apply -1..5 star ratings (-1 = reject, 0 = unrated).

• import_batch(source_path, recursive?) — Register a folder as a film roll.

• list_styles() — Enumerate installed darktable styles (presets), returning name + description per entry.

• apply_preset(photo_ids, preset_name) — Apply a named darktable style to one or more photos. Use list_styles first to discover exact names.

Camera ingest (headless):

• import_from_camera(destination?, camera_port?, timeout_seconds?) — Detect a camera via libgphoto2 and copy photos to a local directory. Auto-merges hybrid setups (one card on PTP, the other mounted as USB Mass-Storage) into a single import — Nikon DSLRs in particular show up that way and the previous behavior silently halved the import.

Vision-rating workflow (headless, file-based — no library required, needs [vision] extra):

• extract_previews(source_dir, output_dir?, max_dim?, thumb_dim?, overwrite?) — Pull auto-rotated JPEG previews + small thumbs out of raws (NEF/CR2/ARW/DNG/...), with an EXIF summary per file. Per-file details (paths, EXIF, errors) land in <output_dir>/.extract_previews.jsonl; the tool response keeps only counts and the side-file path so 700+ NEFs don't overflow the agent's context.

• apply_ratings_batch(source_dir, ratings, log?) — Write XMP xmp:Rating sidecars for a {stem: rating} batch + an append-only ratings.jsonl log.

• open_in_darktable(source_dir, rating?, rating_min?, rating_max?) — Launch the GUI on a folder. Auto-registers as a film roll; pre-applies any rating filter (exact, ≥, ≤, or inner range) via dt.gui.libs.collect.filter.

Export:

• export_images(photo_ids, output_path, format, quality?) — Export to JPEG/PNG/TIFF via darktable-cli. Runs in an isolated config dir under $XDG_CACHE_HOME/darktable-mcp/cli-config/, so exports work even when the GUI is open (no database is locked race against the user's ~/.config/darktable/library.db). Per-file results land in <output_path>/.export_images.jsonl; the tool response is bounded — counts, side-file path, and the first error if any.

Design rules

Use only the official darktable APIs: darktable-cli for export, the Lua API for everything else. No direct library.db reads or writes. Tools that return data to the AI must be headless; the GUI may launch only when the tool's purpose is to show the human something.

Why some tools are parked

darktable-cli doesn't load the user's library and darktable --lua brings up the full GUI, so there's no headless one-shot path for library reads/writes. Iteration 2 (spec: docs/superpowers/specs/2026-04-27-ipc-bridge-mvp-design.md) shipped a long-running Lua plugin loaded into the user's interactive darktable session, with a file-based JSON RPC bridge. The library tools (view_photos, rate_photos, import_batch, list_styles, apply_preset) all ride on it.

adjust_exposure was retired during iteration 3 — see docs/superpowers/specs/2026-04-28-iter3-design.md. The darktable Lua API in 9.6.0 exposes neither image.modules nor image.history, and dt.gui.action requires an active darkroom view (single-image, GUI-driven). The realistic future paths (pre-created .dtstyle exposure presets + apply_preset, or darktable-cli --style for export-only) are workable but not "set +N EV from Lua" tools.

Installation

pip install darktable-mcp
# Optional: vision-rating workflow extras
pip install 'darktable-mcp[vision]'
# Install the Lua plugin into ~/.config/darktable/, then restart darktable
darktable-mcp install-plugin

You also need darktable (with darktable-cli) on PATH. The [vision] extra pulls in rawpy, Pillow, and pyexiv2, which need system libraw and libexiv2.

Configuration

Add to your Claude Desktop config:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "darktable": {
      "command": "darktable-mcp"
    }
  }
}

Vision-rating workflow

When darktable's library doesn't yet know about your shoot — typically straight off a card — you can rate by vision before any import:

1. extract_previews writes auto-rotated JPEGs and an EXIF summary so the client can iterate efficiently.

2. The client reads previews, decides ratings, and calls apply_ratings_batch to write XMP sidecars next to the raws.

3. open_in_darktable launches the GUI with the folder as a film roll, lighttable filtered to the rating range you want.

No SQLite poking, no half-imported state, no GUI launch until step 3.

Requirements

• Python 3.8+

• darktable 4.0+ (with darktable-cli on PATH)

• An MCP-compatible client (Claude Desktop, Claude Code, etc.)

Contributing

Contributions welcome. Any change that reads or writes library.db directly will be rejected.

License

MIT — see LICENSE.