glowfic-rag
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@glowfic-ragSearch planecrash for Keltham explaining expected utility"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 is based on rocurley/glowfic-dl.
Quick start
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.shUsing with Claude Code
The repo includes .mcp.json, which Claude Code auto-detects. From the repo directory:
claudeClaude 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 IarwainAdding 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 --resetThe 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
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