Glowfic Rag

Semantic search over glowfic.com content, exposed as an MCP server for Claude Code and Cursor.

Uses GTE-Large embeddings stored in ChromaDB. The pre-built index covers ~658K posts from:

• planecrash (board 215) — 19K posts, 28 threads
• Sandboxes (board 3) — 639K posts, 2,214 threads
• trainwreck (board 277) — 595 posts, 7 threads

Board structure fetching (src/render.py, src/auth.py) is derived from rocurley/glowfic-dl by Roger Curley and Alyssa Riceman.

Quick start

Requires Python 3.10+ (the mcp package needs it).

git clone https://github.com/keenanpepper/glowfic-rag.git
cd glowfic-rag

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Download the pre-built vector database (~2GB compressed, ~5GB on disk)
./setup_data.sh

Using with Claude Code

The repo includes .mcp.json, which Claude Code auto-detects. From the repo directory:

claude

Claude will have access to search_glowfic and list_indexed_continuities tools.

Using with Cursor

Create .cursor/mcp.json with the absolute path to your clone:

{
  "mcpServers": {
    "glowfic-search": {
      "command": "/path/to/glowfic-rag/.venv/bin/python3",
      "args": ["-m", "rag.mcp_server"],
      "cwd": "/path/to/glowfic-rag"
    }
  }
}

Then reload the Cursor window.

CLI search

You can also search from the command line:

python -m rag.search "Keltham explains expected utility" -k 5
python -m rag.search "vampire meets werewolf" --continuity Sandboxes
python -m rag.search "dath ilan" --author Iarwain

Adding more continuities

Scrape a board to JSONL, then add it to the index:

# Scrape (supports --resume for interrupted runs)
python -m rag.scrape https://glowfic.com/boards/277

# Index (adds to existing collection, doesn't rebuild)
python -m rag.index data/trainwreck.jsonl

# Or reindex everything from scratch
python -m rag.index --reset

The scraper handles pagination for large boards and retries throttled requests automatically.

Performance notes

• First query: ~10 seconds (model load + index warm-up)
• Subsequent queries: < 1 second
• Memory: ~3-5GB when model + index are loaded
• Disk: ~5GB for the ChromaDB index