Quant Brain MCP Server

by paarths-collab Your server? Claim it

answering

Quant Brain MCP is answering right now. Last checked 8 min ago. It exposes 25 tools. Last commit 16 Aug 2026.

Stock analysis MCP: 38 indicators, 7 optimizers, 8 backtests, US + India sectors.

Uptime history 33 days of history · worst day 99%

33 days agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

479 ms

Response time

average over 24h

Stars

last commit 16 Aug 2026

What the code does

We read the source, 18 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

Executes code built at runtime tools/intelligence/plotly_dashboard.py:178

            module = __import__(module_path, fromlist=[fn_name])

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 8 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add quant-brain-mcp --transport http https://mcp-quant-brain.onrender.com/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "quant-brain-mcp": {
      "url": "https://mcp-quant-brain.onrender.com/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.quant-brain-mcp]
url = "https://mcp-quant-brain.onrender.com/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "quant-brain-mcp": {
      "url": "https://mcp-quant-brain.onrender.com/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "quant-brain-mcp": {
      "url": "https://mcp-quant-brain.onrender.com/mcp"
    }
  }
}

Available tools 25

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

analyze

analyze_momentum

Momentum indicators: rsi, macd, roc, cci, stoch, stochrsi, tsi, willr. Runs all momentum indicators for the ticker, or only the subset named in `indicators` (e.g. ["rsi", "macd"]). `period` sets the history window: "1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max" (default "2y").

analyze_sector_intelligence_tool

Analyze sector return, risk, momentum, drawdown and correlation; select best sector.

analyze_statistics

Statistical behavior: log_return, zscore, skew, kurtosis, entropy. Runs all statistical indicators for the ticker, or only the subset named in `indicators`. `period` sets the history window: "1d","5d","1mo","3mo", "6mo","1y","2y","5y","10y","ytd","max" (default "2y").

analyze_technical_levels

Moving averages and price levels: sma, ema, hma, kama, ichimoku, supertrend, vwap, vwma. Runs all level indicators for the ticker, or only the subset named in `indicators`. `period` sets the history window: "1d","5d","1mo","3mo", "6mo","1y","2y","5y","10y","ytd","max" (default "2y").

analyze_trend

Trend strength and direction: adx, aroon, chop, psar, vortex, zigzag. Runs all trend indicators for the ticker, or only the subset named in `indicators`. `period` sets the history window: "1d","5d","1mo","3mo", "6mo","1y","2y","5y","10y","ytd","max" (default "2y").

analyze_volatility

Volatility and bands: atr, bbands, donchian, kc, stdev, ui. Runs all volatility indicators for the ticker, or only the subset named in `indicators`. `period` sets the history window: "1d","5d","1mo","3mo", "6mo","1y","2y","5y","10y","ytd","max" (default "2y").

analyze_volume

Volume confirmation: obv, cmf, mfi, ad, pvt. Runs all volume indicators for the ticker, or only the subset named in `indicators`. `period` sets the history window: "1d","5d","1mo","3mo", "6mo","1y","2y","5y","10y","ytd","max" (default "2y").

backtest

backtest_macd_momentum

Run MACD momentum strategy backtest for one ticker. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

backtest_macd_trend_follower

Run MACD trend follower strategy backtest for one ticker. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

backtest_mean_reversion_rsi_bb

Run RSI + Bollinger Band mean-reversion strategy backtest. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

backtest_rsi_mean_reversion

Run RSI mean-reversion strategy backtest. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

backtest_sma_crossover

Run SMA crossover strategy backtest. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

backtest_trend_crossover

Run trend crossover strategy backtest. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

backtest_volatility_breakout

Run volatility breakout strategy backtest. `period`: history window, e.g. "1y","2y","5y","10y" (default "2y").

generate

generate_chart_pack

Generate the full institutional chart suite for dashboard rendering.

generate_charts

Generate charts for portfolio, strategy, quant, fundamentals, and sector pipeline.

generate_optimized_verdict

Optimize a portfolio and return backtest metrics plus a final verdict. Args: tickers: List of ticker symbols (e.g. ["AAPL", "RELIANCE.NS"]) amount: Investment amount used for allocation context optimize_type: Optimization mode. Supported values are: "mvo", "hrp", "max_sharpe", "min_volatility", "black_litterman", "cvar", "semivariance". period: History window for price data, e.g. "1y", "2y", "5y", "10y" (default "2y"). Longer windows give the optimizer and backtest more data at the cost of a slower fetch.

build

build_trade_plan

Build a sized trade plan: entry, stop, share count, R targets, invalidation. The answer to "what do I do?": given account equity and the percent of it you are willing to risk, returns an entry reference (last close), a structural stop (tighter of recent swing level or 2x ATR, never inside daily noise), the exact number of shares so a stop-out loses only the risk budget, 1R/2R/3R targets, a liquidity check (order as % of 20-day turnover), and a one-line invalidation. Educational analysis, not investment advice.

company

get_company_profile

Return a full company snapshot with business, valuation, and market metadata.

news

get_news

Recent news headlines for a ticker via Yahoo Finance's news feed. Returns structured articles (title, publisher, url, published_at, summary) for you to read and synthesize. Not a scraper -- uses Yahoo's aggregated feed, so coverage is strongest for large-cap US and Indian names.

plot

plot_charts

Alias for chart generation; kept for natural plotting language in clients.

price

price_alert

Persistent price alerts stored server-side (survive restarts, one-shot). Actions: set -- watch a level: price_alert("set", ticker="RELIANCE.NS", level=1270, direction="below", note="stop level") list -- show all active alerts delete -- remove an alert by alert_id check -- fetch current prices for every active alert and return which fired; fired alerts deactivate so they never spam. A server cannot push messages into Claude, so pair this with a scheduled task that calls action='check' on a cadence (e.g. every 30 minutes during market hours) and notifies the user only when 'triggered' is non-empty.

quote

get_quote

Current price snapshot for one or more tickers (US and Indian). Returns last price, day change %, day and 52-week ranges, position within the 52-week range, and volume vs 3-month average, with an `as_of` timestamp. US quotes are near-real-time; NSE/BSE quotes are ~15 minutes delayed (disclosed in the response). For to-the-second Indian prices during market hours, supplement with a live web search.

scan

scan_watchlist

Scan a watchlist and report which names did something actionable. For each ticker: last close, day change, gap, distance from the 20/50/200-day averages and the 52-week high, ATR%, and volume vs its 20-day average -- plus which rules fired (near_52w_high, volume_spike, crossed_above/below_200dma, at_20dma, gapped_over_1atr). Sorted most-actionable first. The Sunday-evening tool: run it over your list, then feed interesting names into build_trade_plan.

sector

find_sector_stock_pipeline_tool

Run a multi-step pipeline: sector performance -> stock ranking -> strategy selection.

Endpoints

URL	Transport	State	Latency	Checked
https://mcp-quant-brain.onrender.com/mcp	streamable-http	answering	663 ms	8 min ago

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Quant Brain MCP — questions

Answers built from our own checks of this server.

What can Quant Brain MCP do?

It exposes 25 tools, read directly from the server on our last check. Among them: analyze_momentum, analyze_sector_intelligence_tool, analyze_statistics, analyze_technical_levels, analyze_trend, analyze_volatility and 19 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

What is Quant Brain MCP mostly used for?

Its tools cluster around analyze, backtest and generate. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.

Is Quant Brain MCP working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 479 ms. The bar chart above shows every period we have measured.

How do I connect Quant Brain MCP?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does Quant Brain MCP need an API key?

No. Quant Brain MCP completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 25 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Quant Brain MCP?

It answers our handshake in 479 ms on average, which is faster than 31% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.

Is Quant Brain MCP open source?

Yes — it is published under the MIT licence, written in Python and 4 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.