Documentation

Quickstart#

The MCP server supports direct SSE and stdio bridge. Pick your client:

Claude Desktop (Zero Local Files)

Claude Desktop communicates over local stdio. Use the modern on-the-fly runner via uvx mcp-proxy (Python) or npx mcp-remote (Node.js) with zero local files or downloads:

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "stoploss": {
      "command": "uvx",
      "args": [
        "mcp-proxy",
        "https://api.stoploss.space/mcp/sse?api_key=sl_live_your_key_here"
      ]
    }
  }
}

Restart Claude Desktop. You will see stoploss connected with all 13 tools in the bottom-left toolbar.

Cursor / VS Code (Native SSE)

Cursor supports direct SSE connection without any local scripts or proxies:

// .cursor/mcp.json  (or .vscode/mcp.json)
{
  "mcpServers": {
    "stoploss": {
      "url": "https://api.stoploss.space/mcp/sse?api_key=sl_live_your_key_here"
    }
  }
}

Python SDK (Remote SSE)

from mcp.client.sse import sse_client
from mcp import ClientSession
import asyncio, os

async def main():
    api_key = os.environ.get("STOPLOSS_API_KEY", "sl_live_your_key_here")
    headers = {"X-API-Key": api_key}
    url = "https://api.stoploss.space/mcp/sse"

    async with sse_client(url, headers=headers) as (r, w):
        async with ClientSession(r, w) as session:
            await session.initialize()

            # Run a natural-language screen
            result = await session.call_tool(
                "screen_stocks",
                {"query": "momentum breakout with high ROE"}
            )
            print(result.content[0].text)

asyncio.run(main())

Authentication#

All requests require a key passed via the X-API-Key header or ?api_key= parameter. Keys are prefixed sl_live_ for production and sl_test_ for sandbox.

Tools Reference#

Every tool returns a structured JSON object. All responses include a data_as_of timestamp. On failure, the response contains {"error": "...", "status": "failed"}.

screen_stocks

Natural-language stock screener. Parses your query into structured filters and runs them against all NSE/BSE equities.

// Example response
{
  "query": "profitable small caps with low debt and momentum",
  "interpreted_query": "market_cap < 5000 AND net_profit > 0 AND debt_equity < 0.5 AND rsi_14 > 55",
  "confidence": 0.91,
  "results": [
    {
      "symbol": "KPITTECH",
      "company_name": "KPIT Technologies Ltd",
      "sector": "IT",
      "pe_ratio": 42.1,
      "roe": 22.3,
      "rsi_14": 68.4,
      "technical_score": 87,
      "trend_direction": "UPTREND"
    }
  ],
  "count": 14,
  "ai_basket_summary": "Growth-oriented small-cap IT and manufacturing names with clean balance sheets...",
  "data_as_of": "2026-09-30T08:00:00Z"
}

run_predefined_screen

Runs one of 35 institutional-grade pre-built screens by name.

batch_screen_stocks

Compare up to 20 tickers side-by-side. Returns a comparison table with rankings.

get_fundamental_analysis

Full forensic analysis of a single stock: Beneish M-Score, Altman Z-Score, Piotroski F-Score, DuPont decomposition, and a quality composite score.

{
  "symbol": "HDFCBANK",
  "company_name": "HDFC Bank Ltd",
  "beneish_m_score": -2.84,
  "beneish_interpretation": "LOW_MANIPULATION_RISK",
  "altman_z_score": 4.21,
  "altman_zone": "SAFE",
  "piotroski_f_score": 8,
  "piotroski_strength": "STRONG",
  "dupont": {
    "net_profit_margin": 0.183,
    "asset_turnover": 0.09,
    "equity_multiplier": 9.8,
    "roe": 0.161
  },
  "quality_score": "A+",
  "moat_rating": "WIDE",
  "recommendation": "BUY",
  "confidence": "92%",
  "data_as_of": "2026-09-30T08:00:00Z"
}

get_technical_analysis

Technical signal suite. Use lite mode for fast pre-computed signals (~50ms), or full mode for deep analysis including VCP detection, Smart Money divergence, and multi-timeframe EMA alignment.

get_advanced_analysis

Institutional-grade quant modules: Smart Money Flow, DCF valuation, Wyckoff phase detection, and optimal position sizing.

get_news_sentiment

Fetches recent news and scores sentiment using NLP. Returns per-article impact scores and aggregated themes. Always live — not cached.

get_optimal_strategy [PRO]

Autonomously discovers the best trading strategy for any NSE stock. Uses Bayesian Optimization (Optuna) distributed across CPU cores with Ray to evaluate 6 canonical strategy archetypes (Trend Follow, Mean Reversion, Momentum, Volatility, Breakout, Combined) filtered through Hidden Markov Model (HMM) regime detection.

run_custom_backtest [PRO]

Backtests a user-described trading strategy on a specific NSE stock. Parses natural language descriptions (e.g. “Buy when RSI drops below 30, sell when it crosses 70, use 2x ATR stop loss”) into strict Pydantic schemas, runs VectorBT simulations with realistic position sizing and slippage, and generates an institutional-grade tearsheet.

get_recommendation

AI-generated BUY / HOLD / SELL verdict synthesising fundamental, technical, and sentiment signals. Pass pre-fetched data to skip re-fetching and reduce latency.

get_peer_comparison

Benchmarks a stock against its sector peers and returns relative percentile ranks.

get_stock_ratios

Returns the full ratio snapshot for a ticker: valuation, profitability, leverage, efficiency, and growth.

list_available_stocks

Returns all NSE/BSE symbols in the database with sector and industry metadata. Use to validate tickers before calling analysis tools.

// No parameters required
{
  "count": 2847,
  "coverage": "NSE + BSE",
  "stocks": [
    { "symbol": "HDFCBANK", "company_name": "HDFC Bank Ltd", "sector": "Financials", "industry": "Private Banks" },
    { "symbol": "RELIANCE", "company_name": "Reliance Industries Ltd", "sector": "Energy", "industry": "Oil & Gas" }
  ],
  "data_as_of": "2026-09-30T08:00:00Z"
}

Rate Limits#

Error Reference#

Changelog#