{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_algovault-crypto-quant-signal-mcp","slug":"algovault-crypto-quant-signal-mcp","name":"crypto-quant-signal-mcp","type":"mcp","url":"https://smithery.ai/servers/algovault/crypto-quant-signal-mcp","page_url":"https://unfragile.ai/algovault-crypto-quant-signal-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:algovault/crypto-quant-signal-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_0","uri":"capability://tool.use.integration.quantitative.signal.generation.for.crypto.markets","name":"quantitative-signal-generation-for-crypto-markets","description":"Generates trading signals by analyzing cryptocurrency market data through quantitative models and technical indicators. The MCP server exposes signal generation as a tool that Claude and other LLM clients can invoke, passing market parameters and receiving structured signal outputs (buy/sell/hold recommendations with confidence scores). Integrates with crypto data providers to fetch OHLCV data, compute indicators (RSI, MACD, Bollinger Bands, etc.), and apply statistical models to produce actionable trading signals.","intents":["I want to get buy/sell signals for crypto assets without building my own quant infrastructure","I need to integrate quantitative analysis into my LLM-powered trading bot or research agent","I want Claude to analyze crypto market conditions and suggest trades based on technical indicators","I need to backtest signal generation logic against historical crypto price data"],"best_for":["crypto traders building LLM-powered trading assistants","quant researchers prototyping signal strategies with Claude","teams integrating quantitative analysis into MCP-based AI agents","developers building crypto trading bots that need signal generation as a service"],"limitations":["Signal quality depends on underlying market data freshness and indicator calibration — stale data produces unreliable signals","No built-in risk management or position sizing — signals are directional only, not portfolio-aware","Limited to technical indicators; no fundamental analysis, on-chain metrics, or sentiment data integration","Real-time signal generation latency depends on data provider API response times and computational complexity of indicators","Backtesting capabilities may be limited to specific time ranges depending on data provider historical depth"],"requires":["MCP client (Claude, other LLM with MCP support)","API credentials for crypto data provider (CoinGecko, Binance, Kraken, or similar)","Network connectivity to fetch real-time or historical market data","Understanding of technical indicators and signal interpretation"],"input_types":["cryptocurrency symbol (e.g., BTC, ETH)","timeframe/interval (1m, 5m, 1h, 1d, etc.)","indicator parameters (RSI period, MACD settings, Bollinger Bands deviation)","optional: historical price data in OHLCV format"],"output_types":["structured signal object with direction (BUY/SELL/HOLD)","confidence score or probability","supporting indicator values (RSI, MACD histogram, Bollinger Band position)","timestamp of signal generation","optional: reasoning or explanation of signal logic"],"categories":["tool-use-integration","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_1","uri":"capability://data.processing.analysis.multi.timeframe.indicator.aggregation","name":"multi-timeframe-indicator-aggregation","description":"Computes technical indicators (RSI, MACD, Bollinger Bands, moving averages, etc.) across multiple timeframes simultaneously and aggregates results into a unified signal consensus. The server accepts a cryptocurrency symbol and returns indicator values for 1m, 5m, 15m, 1h, 4h, 1d timeframes in a single call, enabling multi-timeframe analysis patterns where short-term and long-term trends are evaluated together. Aggregation logic may apply voting or weighting schemes to produce a composite signal strength.","intents":["I want to see if short-term and long-term trends align before placing a trade","I need to check if a signal is confirmed across multiple timeframes to reduce false positives","I want to understand trend strength by comparing indicators across 1h, 4h, and daily timeframes","I need a quick multi-timeframe snapshot for a crypto asset without calling multiple APIs"],"best_for":["swing traders who need multi-timeframe confirmation before entering positions","LLM agents that need to reason about trend alignment across different time horizons","crypto analysts building decision trees that depend on timeframe consensus","trading bots that filter signals based on alignment across multiple timeframes"],"limitations":["Aggregation logic is fixed or limited in customization — may not match trader-specific weighting preferences","Indicator lag increases with longer timeframes, potentially delaying signal generation on daily/weekly charts","No support for custom indicators or user-defined aggregation rules","Computational cost scales with number of timeframes — requesting 6+ timeframes may introduce latency"],"requires":["MCP client with tool-calling support","Crypto data provider API with multi-timeframe OHLCV data","Sufficient API rate limits to fetch data for multiple timeframes in a single request"],"input_types":["cryptocurrency symbol (BTC, ETH, etc.)","optional: specific timeframes to include (default: 1m, 5m, 15m, 1h, 4h, 1d)","optional: indicator parameters (RSI period, MACD fast/slow/signal, Bollinger Bands deviation)"],"output_types":["structured object with indicator values per timeframe","aggregated signal strength (e.g., 5/6 timeframes bullish)","per-timeframe signal direction (BUY/SELL/HOLD)","optional: divergence detection (e.g., short-term bullish, long-term bearish)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_10","uri":"capability://tool.use.integration.mcp.protocol.error.handling.and.validation","name":"mcp-protocol-error-handling-and-validation","description":"Implements comprehensive error handling and input validation for MCP protocol interactions, including parameter schema validation, graceful error responses, and detailed error messages. The server validates all inputs against JSON Schema definitions and returns structured error responses with actionable guidance.","intents":["I want clear error messages when I provide invalid signal parameters","I need to know what parameters a signal accepts before calling it","I want the server to reject invalid requests with helpful error details"],"best_for":["MCP client developers building robust signal request handling","Teams debugging signal integration issues","Developers building error recovery logic"],"limitations":["Error messages are only as good as schema definitions; missing validation rules allow invalid requests","Schema validation adds ~10-50ms latency per request","Error recovery is client-side responsibility; server cannot automatically fix invalid requests"],"requires":["JSON Schema definitions for all signal parameters","Error response format specification","Client-side error handling logic"],"input_types":["any MCP request (tool calls, resource queries)"],"output_types":["structured error responses (error code, message, details)","validation error details (which parameter failed, why, expected format)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_2","uri":"capability://data.processing.analysis.historical.backtest.signal.validation","name":"historical-backtest-signal-validation","description":"Validates signal generation logic against historical cryptocurrency price data by replaying signals across past market conditions and computing performance metrics (win rate, profit factor, max drawdown, Sharpe ratio). The server accepts a signal strategy definition (indicator parameters, entry/exit rules) and a date range, then simulates trades on historical OHLCV data and returns detailed backtest results. Enables rapid iteration on signal parameters without live trading risk.","intents":["I want to test if my signal strategy would have been profitable over the last 6 months","I need to optimize indicator parameters (RSI period, MACD settings) based on historical performance","I want to understand the drawdown and win rate of a signal strategy before deploying it live","I need to compare two different signal strategies on the same historical data"],"best_for":["quant researchers developing and validating signal strategies","traders backtesting parameter changes before live deployment","LLM agents that need to evaluate strategy viability before recommending trades","teams building automated trading systems that require signal validation pipelines"],"limitations":["Backtesting assumes perfect execution and no slippage — real trading will have worse results due to spreads, fees, and execution delays","Historical performance does not guarantee future results; market regimes change and past correlations may not persist","Limited to technical indicator-based strategies; cannot backtest fundamental or sentiment-based signals","Backtesting large date ranges (5+ years) or high-frequency strategies (1m timeframe) may be computationally expensive and slow","No support for portfolio-level backtesting or multi-asset correlation analysis"],"requires":["MCP client","Historical OHLCV data (from CoinGecko, Binance, or similar) covering the backtest period","Signal strategy definition (indicator types, parameters, entry/exit rules)","Date range for backtest (start and end dates)"],"input_types":["cryptocurrency symbol","date range (start_date, end_date)","signal strategy definition (JSON object with indicator types, parameters, entry/exit logic)","optional: position sizing rules, slippage/fee assumptions"],"output_types":["backtest results object with metrics: total trades, win rate, profit factor, max drawdown, Sharpe ratio, cumulative return","trade-by-trade log (entry price, exit price, P&L, duration)","equity curve (cumulative P&L over time)","optional: parameter sensitivity analysis (how metrics change with indicator parameter variations)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_3","uri":"capability://tool.use.integration.real.time.market.data.streaming.integration","name":"real-time-market-data-streaming-integration","description":"Connects to cryptocurrency exchange WebSocket APIs (Binance, Kraken, Coinbase, etc.) to stream real-time OHLCV data, order book updates, and trade ticks. The MCP server maintains persistent WebSocket connections and exposes current market state as queryable tools, allowing Claude to request the latest price, volume, bid-ask spread, or order book depth for any crypto asset. Handles reconnection logic, data buffering, and timestamp synchronization automatically.","intents":["I want to get the current price and volume for a crypto asset in real-time","I need to check the bid-ask spread and order book depth before placing a trade","I want to monitor multiple crypto assets simultaneously and alert when certain conditions are met","I need to feed real-time market data into my trading agent for live decision-making"],"best_for":["traders building real-time trading bots with Claude","crypto analysts who need live market data in their LLM workflows","teams building market monitoring agents that react to price movements","developers integrating live crypto data into MCP-based applications"],"limitations":["WebSocket connections are stateful and may disconnect; reconnection logic adds latency and complexity","Real-time data streaming introduces network latency (typically 100-500ms) — not suitable for ultra-high-frequency trading","Order book depth is limited by exchange API (typically top 20-100 levels) — full order book not available","Different exchanges have different data formats and update frequencies — normalization adds complexity","Persistent connections consume resources; scaling to monitor hundreds of assets may require connection pooling or sharding"],"requires":["MCP client","API credentials (public key sufficient for most exchanges) for at least one crypto exchange","Network connectivity with low latency to exchange WebSocket endpoints","Support for WebSocket protocol in the MCP server runtime"],"input_types":["cryptocurrency symbol or trading pair (BTC/USD, ETH/USDT, etc.)","optional: exchange name (Binance, Kraken, Coinbase, etc.)","optional: data type (OHLCV, trades, order book, ticker)"],"output_types":["current price (bid, ask, last trade)","volume (24h, 1h, current candle)","order book snapshot (top N levels with quantity)","trade tick data (price, quantity, timestamp, buyer/seller initiated)","OHLCV candle data (open, high, low, close, volume for current/previous candles)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_4","uri":"capability://data.processing.analysis.correlation.and.divergence.detection","name":"correlation-and-divergence-detection","description":"Analyzes price correlations and technical indicator divergences across multiple cryptocurrency assets to identify trading opportunities. Computes rolling correlations between asset pairs (e.g., BTC vs ETH, BTC vs altcoins), detects when correlated assets diverge from expected relationships, and identifies bullish/bearish divergences (price makes new high but indicator does not). Returns structured divergence alerts with asset pairs, divergence type, and confidence scores.","intents":["I want to find altcoins that are diverging from Bitcoin's trend for mean-reversion trades","I need to detect when two correlated assets break their historical correlation pattern","I want to identify bullish divergences (price lower high but RSI higher high) as reversal signals","I need to monitor correlation changes across a portfolio to detect regime shifts"],"best_for":["crypto traders using correlation and divergence as trading signals","portfolio managers monitoring cross-asset relationships","LLM agents that reason about relative strength and mean reversion","quant researchers analyzing crypto market structure and relationships"],"limitations":["Correlation analysis requires sufficient historical data (typically 20-100 candles minimum) — short timeframes may have unreliable correlations","Divergence detection is lagging by nature (requires price to make new extreme while indicator does not) — signals may come late in reversals","Correlation regimes change over time; historical correlations may not persist during market stress or volatility spikes","No support for non-linear relationships or regime-dependent correlations","Computational cost scales with number of asset pairs — monitoring 100+ assets may require optimization"],"requires":["MCP client","Historical OHLCV data for multiple cryptocurrency assets","Indicator computation capability (RSI, MACD, etc.) for divergence detection","Sufficient data points to compute meaningful correlations (minimum 20-50 candles)"],"input_types":["list of cryptocurrency symbols to analyze","timeframe (1h, 4h, 1d, etc.)","optional: correlation lookback period (default: 50-100 candles)","optional: divergence types to detect (bullish, bearish, hidden, etc.)"],"output_types":["correlation matrix (asset pairs with correlation coefficients)","divergence alerts (asset pair, divergence type, price/indicator values, confidence)","correlation change alerts (when correlation between assets significantly changes)","optional: visualization data (correlation heatmap, divergence chart coordinates)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_5","uri":"capability://data.processing.analysis.volatility.regime.detection.and.forecasting","name":"volatility-regime-detection-and-forecasting","description":"Analyzes historical volatility patterns to detect current market regime (low volatility, high volatility, volatility expansion, volatility contraction) and forecasts near-term volatility using models like GARCH or exponential weighted moving average (EWMA). Returns current volatility regime classification, volatility percentile (current vol vs historical range), and volatility forecast for next N candles. Enables regime-aware signal filtering and position sizing.","intents":["I want to know if the market is in a high or low volatility regime before placing a trade","I need to adjust position size based on current volatility — smaller positions in high vol, larger in low vol","I want to filter signals to only trade when volatility is in a certain range","I need to forecast volatility to estimate potential drawdown and set stop losses"],"best_for":["traders using volatility-aware position sizing and risk management","LLM agents that need to adjust trading aggressiveness based on market conditions","quant researchers building regime-aware trading strategies","risk managers monitoring volatility changes for portfolio hedging"],"limitations":["Volatility forecasting is inherently uncertain — GARCH and EWMA models may fail during regime shifts or market stress","Regime detection is lagging (requires historical data to identify regime) — may not detect regime changes in real-time","Different volatility measures (realized, implied, historical) may diverge; this tool typically uses realized volatility only","No support for multi-asset volatility correlation or volatility surface analysis","Forecast accuracy degrades significantly beyond 5-10 candles into the future"],"requires":["MCP client","Historical price data (minimum 50-100 candles) to compute volatility statistics","GARCH or EWMA model implementation (may be built into the server)"],"input_types":["cryptocurrency symbol","timeframe (1h, 4h, 1d, etc.)","optional: volatility lookback period (default: 20-50 candles)","optional: forecast horizon (number of candles to forecast, default: 5-10)"],"output_types":["current volatility (annualized or period-based percentage)","volatility percentile (current vol vs historical 0-100 percentile)","regime classification (low vol, normal, high vol, expanding, contracting)","volatility forecast (predicted volatility for next N candles)","optional: volatility bands or confidence intervals"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_6","uri":"capability://automation.workflow.alert.and.notification.rule.engine","name":"alert-and-notification-rule-engine","description":"Allows users to define custom alert rules (e.g., 'alert when BTC price crosses above 50-day MA', 'alert when RSI > 70 on 1h timeframe', 'alert when correlation between BTC and ETH drops below 0.7') and automatically monitors market conditions to trigger notifications. The MCP server maintains rule state, evaluates conditions against real-time or periodic market data, and exposes tools to create, update, delete, and list active alerts. Supports multiple notification channels (webhook, email, Slack, Discord, etc.).","intents":["I want to be notified when a specific price level is reached without constantly monitoring the chart","I need to set up alerts for multiple technical conditions (RSI, MACD, moving average crosses) across different assets","I want to receive alerts in Slack or Discord when my trading signals are triggered","I need to monitor correlation changes and get notified when assets diverge from expected relationships"],"best_for":["traders who want automated monitoring without manual chart watching","LLM agents that need to trigger external actions (webhooks, notifications) based on market conditions","teams building crypto trading platforms with alert functionality","developers integrating market monitoring into larger applications"],"limitations":["Alert evaluation latency depends on polling frequency — real-time alerts may have 1-5 minute delays if using periodic polling instead of WebSocket","Notification delivery is not guaranteed (webhook failures, rate limiting, network issues may cause missed alerts)","Complex rule logic (nested conditions, OR/AND combinations) may be limited or require specific syntax","Stateful alert management requires persistent storage; server restarts may lose alert state if not persisted","No built-in deduplication — rapid price movements may trigger multiple alerts for the same condition"],"requires":["MCP client","Real-time or periodic market data source","Notification endpoint (webhook URL, email service, Slack/Discord webhook, etc.)","Persistent storage for alert rules and state (database or file-based)"],"input_types":["alert rule definition (condition type, parameters, asset, timeframe)","notification channel (webhook URL, email, Slack channel, Discord webhook, etc.)","optional: alert name, description, enabled/disabled flag"],"output_types":["alert creation confirmation (alert ID, status)","alert trigger event (timestamp, condition met, current values)","list of active alerts (rule definitions, creation time, last triggered)","alert history (past triggers, notification delivery status)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_7","uri":"capability://data.processing.analysis.portfolio.performance.and.attribution.analysis","name":"portfolio-performance-and-attribution-analysis","description":"Tracks cryptocurrency portfolio holdings and computes performance metrics (total return, daily/monthly return, Sharpe ratio, max drawdown, win rate) with attribution analysis showing which assets/trades contributed most to gains or losses. Accepts portfolio composition (asset holdings, entry prices, quantities) and compares against market benchmarks (BTC, ETH, market cap-weighted index). Returns detailed performance breakdown by asset and time period.","intents":["I want to see how my crypto portfolio performed compared to holding Bitcoin","I need to understand which trades or assets contributed most to my gains or losses","I want to calculate my portfolio's Sharpe ratio and max drawdown to assess risk-adjusted returns","I need to track performance across different time periods (daily, weekly, monthly) to identify trends"],"best_for":["crypto traders tracking personal portfolio performance","fund managers reporting performance to investors","LLM agents that need to analyze trading results and learn from past decisions","teams building crypto portfolio management platforms"],"limitations":["Attribution analysis assumes static holdings; does not handle complex strategies like options, shorts, or leverage","Performance calculation depends on accurate entry/exit prices and timestamps — data quality issues propagate to results","Benchmark comparison is limited to predefined indices (BTC, ETH, market cap-weighted); custom benchmarks may not be supported","No support for tax-loss harvesting analysis or cost basis tracking","Real-time performance updates may lag if using periodic data snapshots instead of continuous tracking"],"requires":["MCP client","Portfolio composition data (asset symbols, quantities, entry prices, entry timestamps)","Historical price data for portfolio assets and benchmarks","Optional: current market prices for real-time performance calculation"],"input_types":["portfolio holdings (list of assets with quantity, entry price, entry date)","optional: benchmark selection (BTC, ETH, market cap-weighted index, custom)","optional: time period for analysis (start date, end date)"],"output_types":["portfolio performance metrics (total return %, daily/monthly return, Sharpe ratio, max drawdown, win rate)","per-asset performance breakdown (return %, contribution to total return, volatility)","benchmark comparison (portfolio return vs benchmark return, alpha, beta)","trade-by-trade attribution (entry/exit prices, P&L, holding period)","optional: performance chart data (equity curve, drawdown chart)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_8","uri":"capability://data.processing.analysis.sentiment.and.on.chain.data.integration","name":"sentiment-and-on-chain-data-integration","description":"Integrates external data sources (social media sentiment, on-chain metrics, funding rates, open interest) into the signal generation pipeline. Fetches sentiment scores from crypto-specific sources (Twitter/X sentiment, Reddit mentions, news sentiment), on-chain metrics (whale transactions, exchange inflows/outflows, active addresses), and derivatives data (funding rates, open interest, liquidation levels). Combines these signals with technical indicators to produce more robust trading signals.","intents":["I want to incorporate social sentiment into my trading signals to avoid contrarian fades","I need to monitor on-chain metrics like whale transactions and exchange inflows to detect accumulation/distribution","I want to check funding rates and open interest to understand leverage and potential liquidation cascades","I need to combine sentiment, on-chain, and technical signals for a more holistic trading decision"],"best_for":["crypto traders using multi-factor analysis (technical + sentiment + on-chain)","LLM agents that need to reason about market structure and participant behavior","quant researchers building alternative data pipelines","teams building comprehensive crypto trading platforms"],"limitations":["Sentiment data quality varies widely; social media sentiment can be manipulated or gamed by coordinated actors","On-chain data interpretation is complex and context-dependent; whale transactions may not indicate directional intent","Funding rates and open interest are exchange-specific; aggregating across exchanges adds complexity","Data freshness varies by source; some on-chain metrics update slowly (hourly or daily) vs real-time price data","Integration of multiple data sources increases latency and API quota consumption","No built-in weighting or fusion logic for combining disparate signal types"],"requires":["MCP client","API credentials for sentiment data providers (e.g., Santiment, Glassnode, LunarCrush, Nansen)","API credentials for on-chain data providers (Glassnode, Nansen, Chainalysis, etc.)","API credentials for derivatives data (Coinglass, Bybit, Deribit, etc.)"],"input_types":["cryptocurrency symbol","optional: data types to fetch (sentiment, on-chain, derivatives, all)","optional: lookback period for sentiment/on-chain metrics (default: 24h, 7d, 30d)"],"output_types":["sentiment score (aggregated from multiple sources, typically -1 to +1 or 0-100 scale)","on-chain metrics (whale transactions, exchange inflows/outflows, active addresses, MVRV ratio, etc.)","derivatives data (funding rate, open interest, liquidation levels, long/short ratio)","optional: combined signal (technical + sentiment + on-chain consensus)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_algovault-crypto-quant-signal-mcp__cap_9","uri":"capability://data.processing.analysis.signal.performance.attribution.and.analytics","name":"signal-performance-attribution-and-analytics","description":"Analyzes signal performance and attributes returns to specific signal components (trend component, mean-reversion component, volatility component, etc.), providing detailed performance analytics and component contribution metrics. The server decomposes signal performance and identifies which signal components drive profitability.","intents":["I want to understand which parts of my signal drive returns","I need to identify if my signal is profitable due to trend-following or mean-reversion","I want to measure the contribution of each signal component to total returns"],"best_for":["Quant teams analyzing signal composition and effectiveness","Traders understanding signal behavior and reliability","Teams building interpretable trading systems"],"limitations":["Attribution analysis requires decomposable signal architecture; monolithic signals cannot be analyzed","Attribution is approximate; exact decomposition depends on signal design","Computation time scales with historical data length and number of components","Attribution results may not generalize to future market conditions"],"requires":["Signal with identifiable components (trend, mean-reversion, volatility, etc.)","Historical signal values and component values","Market returns for attribution calculation"],"input_types":["signal type, symbol, date range","optional: component weights for custom attribution"],"output_types":["component contribution metrics (% of total returns)","component performance curves (returns from each component)","component correlation with market returns","component stability and consistency metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["MCP client (Claude, other LLM with MCP support)","API credentials for crypto data provider (CoinGecko, Binance, Kraken, or similar)","Network connectivity to fetch real-time or historical market data","Understanding of technical indicators and signal interpretation","MCP client with tool-calling support","Crypto data provider API with multi-timeframe OHLCV data","Sufficient API rate limits to fetch data for multiple timeframes in a single request","JSON Schema definitions for all signal parameters","Error response format specification","Client-side error handling logic"],"failure_modes":["Signal quality depends on underlying market data freshness and indicator calibration — stale data produces unreliable signals","No built-in risk management or position sizing — signals are directional only, not portfolio-aware","Limited to technical indicators; no fundamental analysis, on-chain metrics, or sentiment data integration","Real-time signal generation latency depends on data provider API response times and computational complexity of indicators","Backtesting capabilities may be limited to specific time ranges depending on data provider historical depth","Aggregation logic is fixed or limited in customization — may not match trader-specific weighting preferences","Indicator lag increases with longer timeframes, potentially delaying signal generation on daily/weekly charts","No support for custom indicators or user-defined aggregation rules","Computational cost scales with number of timeframes — requesting 6+ timeframes may introduce latency","Error messages are only as good as schema definitions; missing validation rules allow invalid requests","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4646342993924097,"quality":0.32,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:18:31.930Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=algovault-crypto-quant-signal-mcp","compare_url":"https://unfragile.ai/compare?artifact=algovault-crypto-quant-signal-mcp"}},"signature":"Gt93LymwNK1dvyLeurAxdtM+Vevdez9MvMg1CzMJFxEzQ2bRU8BsJhLYwtKhLLjEbqBB4XTI8PVkN4dFKibDCA==","signedAt":"2026-06-20T06:39:09.789Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/algovault-crypto-quant-signal-mcp","artifact":"https://unfragile.ai/algovault-crypto-quant-signal-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=algovault-crypto-quant-signal-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}