Add danbooru-mcp auto-start, git sync, status API endpoints, navbar status indicators, and LLM format retry

- app.py: add subprocess import; add _ensure_mcp_repo() to clone/pull
  danbooru-mcp from https://git.liveaodh.com/aodhan/danbooru-mcp into
  tools/danbooru-mcp/ at startup; add ensure_mcp_server_running() which
  calls _ensure_mcp_repo() then starts the Docker container if not running;
  add GET /api/status/comfyui and GET /api/status/mcp health endpoints;
  fix call_llm() to retry up to 3 times on unexpected response format
  (KeyError/IndexError), logging the raw response and prompting the LLM
  to respond with valid JSON before each retry
- templates/layout.html: add ComfyUI and MCP status dot indicators to
  navbar; add polling JS that checks both endpoints on load and every 30s
- static/style.css: add .service-status, .status-dot, .status-ok,
  .status-error, .status-checking styles and status-pulse keyframe animation
- .gitignore: add tools/ to exclude the cloned danbooru-mcp repo

data/prompts/checkpoint_system.txt

@@ -1,5 +1,12 @@
 You are a JSON generator for AI image generation model (checkpoint) profiles. Output ONLY valid JSON matching the exact structure below. Do not wrap in markdown code blocks.
 
+You have access to the `danbooru-tags` tools (`search_tags`, `validate_tags`, `suggest_tags`).
+Use these tools when populating the `base_positive` and `base_negative` fields to ensure all tags are valid Danbooru tags.
+- Use `search_tags` or `suggest_tags` to discover the most relevant quality/style tags for this checkpoint.
+- Use `validate_tags` to verify your final tag selection.
+- Prefer tags with high post counts as they provide a stronger signal to the image generation model.
+- Use Danbooru-style tags (underscores instead of spaces, e.g., 'best_quality', 'highly_detailed') for tag values.
+
 Structure:
 {
   "checkpoint_path": "WILL_BE_REPLACED",