{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-chronulus-ai","slug":"chronulus-ai","name":"Chronulus AI","type":"mcp","url":"https://github.com/ChronulusAI/chronulus-mcp","page_url":"https://unfragile.ai/chronulus-ai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-chronulus-ai__cap_0","uri":"capability://tool.use.integration.time.series.forecasting.via.mcp.protocol","name":"time-series forecasting via mcp protocol","description":"Exposes forecasting and prediction capabilities through the Model Context Protocol (MCP), enabling LLM agents to invoke statistical and ML-based time-series models (ARIMA, exponential smoothing, neural networks) without direct API calls. The MCP server acts as a bridge between Claude/other LLMs and underlying forecasting engines, handling schema validation, parameter marshaling, and result serialization through standardized MCP tool definitions.","intents":["I want my AI agent to predict future values in a time series without building custom API integrations","I need to expose forecasting models as callable tools within an agentic workflow","I want to chain forecasting predictions with other LLM reasoning steps in a single agent loop"],"best_for":["AI engineers building multi-step agents that require forecasting as a subtask","teams deploying Claude-based applications that need time-series predictions","developers prototyping forecasting workflows without maintaining separate API infrastructure"],"limitations":["MCP protocol adds serialization/deserialization overhead (~50-200ms per request depending on data size)","Forecasting accuracy depends on underlying model selection and hyperparameter tuning — no automatic model selection exposed","No built-in support for multivariate forecasting or exogenous variables in the base MCP interface","Requires MCP-compatible client (Claude Desktop, custom MCP runners) — not usable via standard REST APIs"],"requires":["MCP client implementation (Claude Desktop, or custom Node.js/Python MCP runner)","Time-series data in structured format (CSV, JSON arrays, or database connection)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation language)"],"input_types":["structured time-series data (JSON arrays, CSV rows)","forecast parameters (horizon length, confidence intervals, model type)","historical data points with timestamps"],"output_types":["point forecasts (single predicted values)","probabilistic forecasts (confidence intervals, quantiles)","structured JSON with predictions and metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_1","uri":"capability://data.processing.analysis.multi.model.forecasting.orchestration","name":"multi-model forecasting orchestration","description":"Abstracts multiple forecasting algorithms (ARIMA, exponential smoothing, Prophet, neural networks) behind a unified interface, allowing agents to request predictions without specifying the underlying model. The system likely implements model selection logic (based on data characteristics, error metrics, or user hints) and may ensemble multiple models for improved robustness. Handles model initialization, training on historical data, and prediction generation with configurable parameters.","intents":["I want to forecast without choosing a specific model — let the system pick the best one","I need ensemble predictions that combine multiple forecasting approaches","I want to compare forecasts from different models to understand prediction uncertainty"],"best_for":["non-expert users who want forecasting without ML model selection overhead","agents that need robust predictions across diverse time-series types","applications requiring uncertainty quantification via model diversity"],"limitations":["Model selection heuristics may not be optimal for domain-specific data (e.g., financial vs. weather time series)","Ensemble predictions increase computational cost (training multiple models sequentially or in parallel)","No explicit support for seasonal decomposition or trend extraction — relies on model-internal handling","Training time scales with historical data length; very long series (>10k points) may incur latency"],"requires":["Historical time-series data with at least 20-50 observations (depending on model)","Timestamps or regular frequency specification","Forecasting libraries (statsmodels, Prophet, TensorFlow/PyTorch for neural models)"],"input_types":["univariate time-series (single value per timestamp)","historical data with regular or irregular timestamps","optional model hints or constraints"],"output_types":["point forecasts from multiple models","ensemble predictions (mean, median, weighted average)","per-model confidence intervals and error metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_2","uri":"capability://planning.reasoning.agent.driven.forecast.refinement.and.retraining","name":"agent-driven forecast refinement and retraining","description":"Enables agents to iteratively improve forecasts by providing feedback, adjusting parameters, or triggering model retraining with new data. The system tracks forecast accuracy over time, allows agents to request alternative models or parameter configurations, and supports incremental retraining workflows where new observations are incorporated into the model without full recomputation. Implements feedback loops where agent-observed outcomes inform future forecast adjustments.","intents":["I want my agent to learn from forecast errors and improve predictions over time","I need to retrain forecasting models as new data arrives without stopping the agent","I want the agent to explore different forecasting parameters and pick the best configuration"],"best_for":["long-running agents that make repeated forecasts and need adaptive behavior","systems with streaming or regularly-updated time-series data","applications where forecast accuracy is critical and continuous improvement is required"],"limitations":["Retraining overhead scales with data size; frequent retraining may introduce latency spikes","No built-in mechanism to detect concept drift or model degradation — relies on agent to monitor accuracy","Incremental learning support depends on underlying model (some models like ARIMA require full retraining)","Agent must manage state across retraining cycles; no automatic state persistence"],"requires":["Mechanism to track forecast accuracy (ground truth data or agent feedback)","Ability to store and version models between retraining cycles","Computational resources for periodic model retraining (CPU/GPU)"],"input_types":["new time-series observations for retraining","agent feedback on forecast quality (binary or numeric scores)","parameter adjustment requests from agent"],"output_types":["updated model weights or parameters","retraining metrics (loss, accuracy improvement)","new forecasts from retrained model"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_3","uri":"capability://data.processing.analysis.structured.forecast.result.parsing.and.validation","name":"structured forecast result parsing and validation","description":"Parses forecasting model outputs into structured, validated formats that agents can reliably consume. Implements schema validation to ensure forecasts conform to expected types (point estimates, confidence intervals, quantiles), handles edge cases (NaN, infinite values, out-of-range predictions), and provides metadata about forecast quality (model used, training data size, confidence level). Enables agents to programmatically reason about forecast reliability and make decisions based on prediction uncertainty.","intents":["I need to validate that forecast outputs are in the expected format before using them in downstream logic","I want to extract confidence intervals and uncertainty metrics from forecasts to inform decision-making","I need to handle edge cases where forecasting models produce invalid or unreliable predictions"],"best_for":["agents that chain forecasting with other decision-making steps and need reliable data contracts","systems requiring audit trails of forecast quality and model confidence","applications where forecast uncertainty directly impacts downstream actions"],"limitations":["Validation rules are fixed at schema definition time; no runtime adaptation to data characteristics","Parsing overhead is minimal but adds latency for very high-frequency forecasting (>1000 requests/sec)","No automatic recovery from invalid forecasts — agent must handle validation failures","Metadata richness depends on underlying forecasting model; some models provide limited uncertainty estimates"],"requires":["Schema definition for expected forecast format (JSON Schema or similar)","Validation library (e.g., Pydantic, Zod, JSON Schema validators)"],"input_types":["raw forecasting model outputs (arrays, dictionaries, or model objects)","schema definitions for validation"],"output_types":["validated forecast objects with typed fields","metadata (confidence level, model type, training data info)","validation error messages if parsing fails"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_4","uri":"capability://planning.reasoning.agent.accessible.forecast.explanation.and.diagnostics","name":"agent-accessible forecast explanation and diagnostics","description":"Exposes forecasting model internals (feature importance, trend/seasonality decomposition, residual analysis) as agent-callable tools, enabling agents to understand why predictions were made and diagnose forecast quality. Implements model-agnostic explanation techniques (SHAP, LIME for neural models; coefficient inspection for statistical models) and provides time-series-specific diagnostics (autocorrelation of residuals, stationarity tests, seasonality strength). Allows agents to request detailed explanations for specific forecasts or model behavior.","intents":["I want my agent to explain why a forecast was made — what patterns did the model detect?","I need diagnostics to understand if a forecasting model is behaving correctly","I want the agent to identify when forecasts are unreliable due to data quality or model issues"],"best_for":["agents in regulated industries (finance, healthcare) where forecast explainability is required","debugging and monitoring systems where agents need to diagnose forecasting failures","applications where agents must communicate forecast reasoning to human stakeholders"],"limitations":["Explanation generation adds computational overhead (especially for neural models using SHAP)","Explanations are model-specific; no unified explanation format across different forecasting algorithms","Diagnostics are statistical in nature and may not capture domain-specific issues","SHAP/LIME explanations for neural models can be slow for high-dimensional time series (>1000 features)"],"requires":["Explanation libraries (SHAP, LIME, or model-specific introspection tools)","Access to training data for residual analysis and diagnostics","Computational resources for explanation generation (especially for neural models)"],"input_types":["forecast request or specific prediction to explain","diagnostic query (e.g., 'check stationarity', 'analyze residuals')"],"output_types":["feature importance or coefficient values","trend/seasonality decomposition","residual diagnostics (ACF, PACF, normality tests)","natural language explanation of forecast drivers"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_5","uri":"capability://planning.reasoning.multi.horizon.and.scenario.based.forecasting","name":"multi-horizon and scenario-based forecasting","description":"Supports forecasting across multiple time horizons (short-term, medium-term, long-term) and conditional scenarios (e.g., 'forecast under 20% demand increase'). Implements scenario branching where agents can request forecasts under different assumptions or constraints, and aggregates multi-horizon predictions into coherent narratives. Handles horizon-specific model selection (e.g., ARIMA for short-term, structural models for long-term) and manages forecast degradation as horizon extends.","intents":["I need forecasts at multiple time horizons (1-week, 1-month, 1-year) for different planning purposes","I want to forecast under different scenarios (e.g., best-case, worst-case, base-case) to inform risk analysis","I need the agent to understand how forecast uncertainty grows with prediction horizon"],"best_for":["strategic planning agents that need long-term forecasts alongside operational predictions","risk analysis and scenario planning systems","agents supporting multi-level decision-making (tactical, strategic, operational)"],"limitations":["Long-horizon forecasts (>12 months) have inherently high uncertainty; model selection becomes critical","Scenario branching increases computational cost (multiple model runs per request)","No automatic scenario generation — agent must specify scenarios explicitly","Reconciling multi-horizon forecasts (ensuring short-term forecasts align with long-term trends) requires post-processing"],"requires":["Sufficient historical data to train models for each horizon (typically 2-3x the longest forecast horizon)","Scenario definitions or constraints that agents can specify","Reconciliation logic if multi-horizon coherence is required"],"input_types":["forecast horizons (list of time periods to predict)","scenario definitions (parameter values, constraints, or conditional statements)","historical time-series data"],"output_types":["multi-horizon forecasts (separate predictions for each horizon)","scenario-specific forecasts (predictions under different assumptions)","uncertainty estimates that increase with horizon length"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_6","uri":"capability://automation.workflow.real.time.streaming.data.integration.for.forecasting","name":"real-time streaming data integration for forecasting","description":"Integrates with streaming data sources (APIs, message queues, databases) to continuously update forecasting models with new observations. Implements incremental model updates that incorporate new data without full retraining, handles out-of-order or delayed data, and maintains forecast freshness as new information arrives. Allows agents to trigger forecasts on-demand with the latest available data, and supports windowed or sliding-window model updates for computational efficiency.","intents":["I want forecasts that always use the most recent data without waiting for batch retraining","I need to handle streaming data that arrives continuously and update forecasts in real-time","I want the agent to detect when new data significantly changes forecasts and alert on major shifts"],"best_for":["real-time monitoring and alerting systems where forecast freshness is critical","agents processing continuous data streams (IoT, financial markets, web analytics)","systems requiring sub-second forecast latency with up-to-date data"],"limitations":["Incremental updates may accumulate errors over time; periodic full retraining is recommended","Handling out-of-order data requires buffering and reprocessing, adding complexity","Streaming integration adds operational overhead (managing data pipelines, error handling)","Some forecasting models (e.g., ARIMA) are not designed for incremental updates and require workarounds"],"requires":["Streaming data source (Kafka, Redis, API, database with change streams)","Incremental learning capability in forecasting models or workarounds (e.g., sliding windows)","Infrastructure for managing streaming pipelines (e.g., Kafka, Flink, or custom event handlers)"],"input_types":["streaming data events (JSON, Avro, or custom formats)","data source configuration (connection strings, topics, schemas)"],"output_types":["updated forecasts with latest data incorporated","change notifications when new data significantly alters predictions","data freshness metadata (timestamp of last update)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chronulus-ai__cap_7","uri":"capability://planning.reasoning.agent.driven.forecast.comparison.and.model.evaluation","name":"agent-driven forecast comparison and model evaluation","description":"Provides agents with tools to compare forecasts from different models, evaluate model performance on historical data (backtesting), and select optimal models based on custom metrics. Implements cross-validation, walk-forward validation, and other evaluation techniques that agents can invoke to assess forecast quality. Allows agents to define custom evaluation metrics and request model comparisons based on specific criteria (e.g., 'minimize worst-case error', 'maximize precision for peaks').","intents":["I want to compare forecasts from different models to understand which is most reliable","I need to backtest models on historical data to assess their performance before using them","I want the agent to automatically select the best model based on custom evaluation criteria"],"best_for":["agents that need to make model selection decisions based on data-driven evaluation","systems where forecast quality directly impacts business outcomes and model validation is critical","research and experimentation workflows where agents explore multiple forecasting approaches"],"limitations":["Backtesting is computationally expensive for large datasets or many models; can introduce latency","Historical performance does not guarantee future performance (especially in non-stationary data)","Custom evaluation metrics require agent specification; no automatic metric selection","Walk-forward validation requires sufficient historical data (typically 3-5x the forecast horizon)"],"requires":["Historical time-series data with ground truth for backtesting","Multiple forecasting models to compare","Evaluation metrics library (MAE, RMSE, MAPE, custom metrics)"],"input_types":["list of models to compare","evaluation metrics (standard or custom)","historical data for backtesting","validation strategy (cross-validation, walk-forward, etc.)"],"output_types":["performance metrics for each model","model comparison rankings","recommended model based on evaluation criteria","detailed evaluation reports (per-period errors, metric breakdowns)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude Desktop, or custom Node.js/Python MCP runner)","Time-series data in structured format (CSV, JSON arrays, or database connection)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation language)","Historical time-series data with at least 20-50 observations (depending on model)","Timestamps or regular frequency specification","Forecasting libraries (statsmodels, Prophet, TensorFlow/PyTorch for neural models)","Mechanism to track forecast accuracy (ground truth data or agent feedback)","Ability to store and version models between retraining cycles","Computational resources for periodic model retraining (CPU/GPU)","Schema definition for expected forecast format (JSON Schema or similar)"],"failure_modes":["MCP protocol adds serialization/deserialization overhead (~50-200ms per request depending on data size)","Forecasting accuracy depends on underlying model selection and hyperparameter tuning — no automatic model selection exposed","No built-in support for multivariate forecasting or exogenous variables in the base MCP interface","Requires MCP-compatible client (Claude Desktop, custom MCP runners) — not usable via standard REST APIs","Model selection heuristics may not be optimal for domain-specific data (e.g., financial vs. weather time series)","Ensemble predictions increase computational cost (training multiple models sequentially or in parallel)","No explicit support for seasonal decomposition or trend extraction — relies on model-internal handling","Training time scales with historical data length; very long series (>10k points) may incur latency","Retraining overhead scales with data size; frequent retraining may introduce latency spikes","No built-in mechanism to detect concept drift or model degradation — relies on agent to monitor accuracy","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=chronulus-ai","compare_url":"https://unfragile.ai/compare?artifact=chronulus-ai"}},"signature":"aMaadOSRcDwoFqJR3MFSbJSoC02qZHYhYaW2gKO0A2Mktt6sb/jXTlaVxcOsTsYgsCTGK2KZ9W06BkqLGrV0AQ==","signedAt":"2026-06-22T03:52:39.780Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chronulus-ai","artifact":"https://unfragile.ai/chronulus-ai","verify":"https://unfragile.ai/api/v1/verify?slug=chronulus-ai","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"}}