# quantifyme MCP server

Describe a trading strategy in plain English and deploy a live signal model in one call. No signup.

## Links
- Registry page: https://www.getdrio.com/mcp/ai-quantifyme-quantifyme
- Website: https://quantifyme.ai

## Install
- Endpoint: https://mcp.quantifyme.ai/mcp
- Auth: Not captured

## Setup notes
- Remote endpoint: https://mcp.quantifyme.ai/mcp

## Tools
- one_shot (Deploy Strategy (generate + backtest + go live)) - End-to-end deploy: generate strategy → train → deploy live.

    One of `prompt` (free-form NL), `preset` (curated winning strategy), or
    `community_id` (copy a published community strategy) is required. If more
    than one is passed, precedence is community_id > preset > prompt.

    Args:
      prompt: Natural-language strategy description (e.g. "Buy when RSI < 30, sell > 70").
      symbol: Currency pair to backtest on. One of: EURUSD, USDJPY, GBPUSD,
        USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD.
      timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min.
      claude_model: Which Claude variant to use for code generation.
        "sonnet" (default — best quality, 1/day free) or "haiku" (faster, 3/day free).
        Ignored when `preset` is set (no generation needed).
      preset: Curated winning-strategy slug. Skips Claude generation entirely
        — deploys a pre-saved strategy known to backtest well on the chosen
        symbol. Available slugs: ema_cross_fast, momentum, scalper_stack,
        sma_only, trend_ema, volatility, bb_squeeze, all_mix, pivot_kid_ema.
        Not every slug exists for every symbol — call list_models afterwards
        to confirm what deployed.
      community_id: Copy-trade a published community strategy. Pass the `id`
        of an entry from `browse_community`. Loads that exact strategy code,
        skips Claude generation, then trains + deploys it. `symbol`/`timeframe`
        still apply to the backtest+deploy.
      webhook_url: Optional webhook to receive live signals.
      telegram_chat_id: Optional Telegram chat ID for signal delivery.

    Returns:
      dict with:
        - live_url (str): tap-through to the deployed model on the qm dashboard.
        - model, stem (str): model identifiers (e.g. "14_EURUSD_15min_Model_24").
        - channels (list[str]): delivery channels active (e.g. ["webhook"]).
        - train_stats (dict, optional): in-sample backtest metrics from the
          training run — keys are some subset of {trades, win_rate,
          profit_factor, return, sharpe, max_drawdown}. SHOW THESE IN YOUR
          REPLY so the user immediately sees how the model backtested.
        - progress (list[str]): per-stage progress log.
     Endpoint: https://mcp.quantifyme.ai/mcp
- list_models (List Trained Models) - List the user's trained models with pre-computed train/test stats. Endpoint: https://mcp.quantifyme.ai/mcp
- list_deployed (List Deployed Live Models) - List the user's currently deployed (live) models. Endpoint: https://mcp.quantifyme.ai/mcp
- generate_strategy (Generate Strategy Code (no training)) - Generate Python strategy code (no training/deploy). Use when the user wants raw code.

    Args:
      features: NL description of features (e.g. "RSI 14, Bollinger Bands").
      signals: NL description of signal logic (e.g. "Buy when RSI < 30").
      model: ML model name (default Random Forest).
      risk: NL risk rules (e.g. "0.5% stop loss").
      description: Optional one-line summary; treated as PRIMARY USER REQUEST.
      symbol: Currency pair the code should target. One of: EURUSD, USDJPY,
        GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD.
      timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min.
      claude_model: "sonnet" (default) or "haiku" (faster, higher daily cap).
     Endpoint: https://mcp.quantifyme.ai/mcp
- browse_community (Browse Community Strategies (leaderboard)) - Browse the public community leaderboard of published strategies, ranked
    by a composite performance score (best first). No signup or key needed.

    Copy-trade flow: call this to find a top strategy, then pass its `id` to
    `one_shot` as `community_id` to deploy a live signal model running that
    exact strategy in one call.

    Args:
      limit: How many top entries to return (default 20, max 200).

    Returns:
      dict with:
        - scripts (list[dict]): ranked entries, best first. Each has:
            id (int — pass to one_shot as community_id), username, title,
            description, created_at, score, and metrics {total_ret,
            sharpe_strat, win_rate, n_trades, mdd, profit_factor}. SHOW the
            top few with their win_rate / total_ret so the user can pick one.
        - count (int).
     Endpoint: https://mcp.quantifyme.ai/mcp

## Resources
Not captured

## Prompts
Not captured

## Metadata
- Owner: ai.quantifyme
- Version: 1.0.0
- Runtime: Streamable Http
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: May 20, 2026
- Source: https://registry.modelcontextprotocol.io