rays-mcp
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., "@rays-mcpShow me market breadth and VIX for major US and Asian indices"
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.
rays-mcp
An MCP server exposing global market breadth, volume, RSI, volatility and sentiment for 12 major equity indices (US + Asia: S&P 500, Nasdaq 100, SOX, Russell 2000, Hang Seng, CSI 300/1000, ChiNext, Nikkei 225, Topix, Taiwan, KOSPI 200).
Companion to asian-etf-mcp; same two-layer design (data layer + thin FastMCP wrapper), same cache + refresh-on-use model.
Tools
tool | returns |
| the 12 indices + tickers (call first) |
| % of constituents above 50/200-day MA, % above/below both, advancers/decliners |
| real trading volume (constituent-aggregated) vs 20-day average + deviation |
| RSI(14) per index + overbought/oversold signal |
| VIX (+ >30 alert), GLD/VIX momentum, AAII bull/neutral/bear survey |
Every result carries source (with as_of + computed_at).
Related MCP server: CryptoDataAPI MCP Server
How it works
src/server.py FastMCP wrapper (5 tools)
src/data_access.py reads cache.json; refresh-on-use keeps it current
build_cache.py assembles cache.json from 3 sources:
configs/indices.json index definitions (committed)cache.json is assembled from:
breadth_cache.json— breadth / advance-decline / real volume. The heavy constituent aggregation (Topix ≈ 1,574 stocks!) is precomputed daily on the rays server, so the MCP reads it rather than recomputing.yfinance — RSI(14, Wilder), VIX, GLD/VIX momentum (computed live).
aaii_sentiment.xls— the weekly AAII investor sentiment survey.
When a tool is called and the cache is behind the latest trading day, the server
rebuilds it first (sync_data.sh + build_cache.py) — no cron needed.
Quick start
python3.10+ -m venv .venv
./.venv/bin/pip install -r requirements.txt
./.venv/bin/python build_cache.py # assemble the cacheUse with Claude Code
claude mcp add rays -- /abs/path/.venv/bin/python /abs/path/src/server.pyThen ask, e.g. “use rays — which markets have the broadest participation and is the VIX elevated?”
Data access note
The breadth/volume layer is computed on the maintainer's rays server (constituent
aggregation across thousands of stocks is too heavy to recompute on demand).
sync_data.sh pulls that precomputed file via SSH. The RSI / VIX / GLD layer is
fetched from public Yahoo Finance, so it works anywhere; AAII comes from the synced
weekly file. To run fully standalone, supply a data/breadth_cache.json produced by
the rays-dashboard pipeline.
Configuration
env var | default | meaning |
|
| index definitions (committed) |
|
| synced/generated data (gitignored) |
|
| rays server for |
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Maintenance
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