Add Checkpoints Gallery with per-checkpoint generation settings
- New Checkpoint model (slug, name, checkpoint_path, data JSON, image_path) - sync_checkpoints() loads metadata from data/checkpoints/*.json and falls back to template defaults for models without a JSON file - _apply_checkpoint_settings() applies per-checkpoint steps, CFG, sampler, base positive/negative prompts, and VAE (with dynamic VAELoader node injection for non-integrated VAEs) to the ComfyUI workflow - Bulk Create from Checkpoints: scans Illustrious/Noob model directories, reads matching HTML files, uses LLM to populate metadata, falls back to template defaults when no HTML is present - Gallery index with batch cover generation and WebSocket progress bar - Detail page showing Generation Settings and Base Prompts cards - Checkpoints nav link added to layout - New data/prompts/checkpoint_system.txt LLM system prompt - Updated README with all current galleries and file structure - Also includes accumulated action/scene JSON updates, new actions, and other template/generator improvements from prior sessions Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Aodhan Collins
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TAG_MCP_README.md
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TAG_MCP_README.md
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# danbooru-mcp
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An MCP (Model Context Protocol) server that lets an LLM search, validate, and get suggestions for valid **Danbooru tags** — the prompt vocabulary used by Illustrious and other Danbooru-trained Stable Diffusion models.
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Tags are scraped directly from the **Danbooru public API** and stored in a local SQLite database with an **FTS5 full-text search index** for fast prefix/substring queries. Each tag includes its post count, category, and deprecation status so the LLM can prioritise well-used, canonical tags.
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---
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## Tools
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| Tool | Description |
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|------|-------------|
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| `search_tags(query, limit=20, category=None)` | Prefix/full-text search — returns rich tag objects ordered by relevance |
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| `validate_tags(tags)` | Exact-match validation — splits into `valid`, `deprecated`, `invalid` |
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| `suggest_tags(partial, limit=10, category=None)` | Autocomplete for partial tag strings, sorted by post count |
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### Return object shape
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All tools return tag objects with:
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```json
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{
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"name": "blue_hair",
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"post_count": 1079908,
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"category": "general",
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"is_deprecated": false
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}
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```
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### Category filter values
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`"general"` · `"artist"` · `"copyright"` · `"character"` · `"meta"`
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---
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## Setup
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### 1. Install dependencies
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```bash
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pip install -e .
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```
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### 2. Build the SQLite database (scrapes the Danbooru API)
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```bash
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python scripts/scrape_tags.py
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```
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This scrapes ~1–2 million tags from the Danbooru public API (no account required)
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and stores them in `db/tags.db` with a FTS5 index.
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Estimated time: **5–15 minutes** depending on network speed.
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```
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Options:
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--db PATH Output database path (default: db/tags.db)
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--workers N Parallel HTTP workers (default: 4)
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--max-page N Safety cap on pages (default: 2500)
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--no-resume Re-scrape all pages from scratch
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--no-fts Skip FTS5 rebuild (for incremental runs)
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```
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The scraper is **resumable** — if interrupted, re-run it and it will
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continue from where it left off.
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### 3. (Optional) Test API access first
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```bash
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python scripts/test_danbooru_api.py
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```
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### 4. Run the MCP server
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```bash
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python src/server.py
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```
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---
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## Docker
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### Quick start (pre-built DB — recommended)
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Use this when you've already run `python scripts/scrape_tags.py` and have `db/tags.db`:
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```bash
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# Build image with the pre-built DB baked in (~30 seconds)
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docker build -f Dockerfile.prebuilt -t danbooru-mcp .
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# Verify
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docker run --rm --entrypoint python danbooru-mcp \
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-c "import sqlite3,sys; c=sqlite3.connect('/app/db/tags.db'); sys.stderr.write(str(c.execute('SELECT COUNT(*) FROM tags').fetchone()[0]) + ' tags\n')"
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```
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### Build from scratch (runs the scraper during Docker build)
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```bash
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# Scrapes the Danbooru API during build — takes ~15 minutes
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docker build \
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--build-arg DANBOORU_USER=your_username \
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--build-arg DANBOORU_API_KEY=your_api_key \
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-t danbooru-mcp .
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```
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### MCP client config (Docker)
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```json
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{
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"mcpServers": {
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"danbooru-tags": {
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"command": "docker",
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"args": ["run", "--rm", "-i", "danbooru-mcp:latest"]
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}
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}
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}
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```
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---
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## MCP Client Configuration
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### Claude Desktop (`claude_desktop_config.json`)
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```json
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{
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"mcpServers": {
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"danbooru-tags": {
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"command": "python",
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"args": ["/absolute/path/to/danbooru-mcp/src/server.py"]
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}
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}
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}
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```
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### Custom DB path via environment variable
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```json
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{
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"mcpServers": {
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"danbooru-tags": {
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"command": "python",
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"args": ["/path/to/src/server.py"],
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"env": {
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"DANBOORU_TAGS_DB": "/custom/path/to/tags.db"
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}
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}
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}
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}
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```
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---
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## Example LLM Prompt Workflow
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```
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User: Generate a prompt for a girl with blue hair and a sword.
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LLM calls validate_tags(["1girl", "blue_hairs", "sword", "looking_at_vewer"])
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→ {
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"valid": ["1girl", "sword"],
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"deprecated": [],
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"invalid": ["blue_hairs", "looking_at_vewer"]
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}
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LLM calls suggest_tags("blue_hair", limit=3)
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→ [
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{"name": "blue_hair", "post_count": 1079908, "category": "general"},
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{"name": "blue_hairband", "post_count": 26905, "category": "general"},
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...
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]
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LLM calls suggest_tags("looking_at_viewer", limit=1)
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→ [{"name": "looking_at_viewer", "post_count": 4567890, "category": "general"}]
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Final validated prompt: 1girl, blue_hair, sword, looking_at_viewer
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```
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---
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## Project Structure
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```
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danbooru-mcp/
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├── data/
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│ └── all_tags.csv # original CSV export (legacy, replaced by API scrape)
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├── db/
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│ └── tags.db # SQLite DB (generated, gitignored)
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├── plans/
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│ └── danbooru-mcp-plan.md # Architecture plan
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├── scripts/
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│ ├── scrape_tags.py # API scraper → SQLite (primary)
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│ ├── import_tags.py # Legacy CSV importer
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│ └── test_danbooru_api.py # API connectivity tests
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├── src/
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│ └── server.py # MCP server
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├── pyproject.toml
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├── .gitignore
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└── README.md
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```
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---
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## Requirements
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- Python 3.10+
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- `mcp[cli]` — official Python MCP SDK
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- `requests` — HTTP client for API scraping
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- `sqlite3` — Python stdlib (no install needed)
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