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by ChronulusAI • Analytics & Monitoring
An MCP server for interfacing with Chronulus AI Forecasting & Prediction Agents.
Interact with Chronulus AI for making forecasts and predictions.
Incorporate AI-driven insights into desktop applications.
Customized AI agent environments through Claude configuration.
The Chronulus MCP Server allows integration with Chronulus AI agents for forecasting and prediction. It supports multiple installation methods including pip, Docker, and uvx. Ideal for those using Claude on desktop platforms, it offers customization options through a JSON configuration file.
A tool that creates a new Chronulus Session and returns a session_id When to use this tool: - Use this tool when a user has requested a forecast or prediction for a new use case - Before calling this tool make sure you have enough information to write a well-defined situation and task. You might need to ask clarifying questions in order to get this from the user. - The same session_id can be reused as long as the situation and task remain the same - If user wants to forecast a different use case, create a new session and then use that How to use this tool: - To create a session, you need to provide a situation and task that describe the forecasting use case - If the user has not provided enough detail for you to decompose the use case into a situation (broad or background context) and task (specific requirements for the forecast), ask them to elaborate since more detail will result in a better / more accurate forecast. - Once created, this will generate a unique session_id that can be used to when calling other tools about this use case.
This tool creates a NormalizedForecaster agent with your session and input data model and then provides a forecast input data to the agent and returns the prediction data and text explanation from the agent. When to use this tool: - Use this tool to request a forecast from Chronulus - This tool is specifically made to forecast values between 0 and 1 and does not require historical data - The prediction can be thought of as seasonal weights, probabilities, or shares of something as in the decimal representation of a percent How to use this tool: - First, make sure you have a session_id for the forecasting or prediction use case. - Next, think about the features / characteristics most suitable for producing the requested forecast and then create an input_data_model that corresponds to the input_data you will provide for the thing being forecasted. - Remember to pass all relevant information to Chronulus including text and images provided by the user. - If a user gives you files about a thing you are forecasting or predicting, you should pass these as inputs to the agent using one of the following types: - ImageFromFile - List[ImageFromFile] - TextFromFile - List[TextFromFile] - PdfFromFile - List[PdfFromFile] - If you have a large amount of text (over 500 words) to pass to the agent, you should use the Text or List[Text] field types - Finally, add information about the forecasting horizon and time scale requested by the user - Assume the dates and datetimes in the prediction results are already converted to the appropriate local timezone if location is a factor in the use case. So do not try to convert from UTC to local time when plotting. - When plotting the predictions, use a Rechart time series with the appropriate axes labeled and with the prediction explanation displayed as a caption below the plot
This tool creates a NormalizedForecaster agent with your session and input data model and then provides a forecast input data to the agent and returns the prediction data and text explanation from the agent. When to use this tool: - Use this tool to request a forecast from Chronulus - This tool is specifically made to forecast values between 0 and 1 and does not require historical data - The prediction can be thought of as seasonal weights, probabilities, or shares of something as in the decimal representation of a percent How to use this tool: - First, make sure you have a session_id for the forecasting or prediction use case. - Next, think about the features / characteristics most suitable for producing the requested forecast and then create an input_data_model that corresponds to the input_data you will provide for the thing being forecasted. - Remember to pass all relevant information to Chronulus including text and images provided by the user. - If a user gives you files about a thing you are forecasting or predicting, you should pass these as inputs to the agent using one of the following types: - ImageFromFile - List[ImageFromFile] - TextFromFile - List[TextFromFile] - PdfFromFile - List[PdfFromFile] - If you have a large amount of text (over 500 words) to pass to the agent, you should use the Text or List[Text] field types - Finally, add information about the forecasting horizon and time scale requested by the user - Assume the dates and datetimes in the prediction results are already converted to the appropriate local timezone if location is a factor in the use case. So do not try to convert from UTC to local time when plotting. - When plotting the predictions, use a Rechart time series with the appropriate axes labeled and with the prediction explanation displayed as a caption below the plot
A tool that rescales the prediction data (values between 0 and 1) from the NormalizedForecaster agent to scale required for a use case When to use this tool: - Use this tool when there is enough information from the user or use cases to determine a reasonable min and max for the forecast predictions - Do not attempt to rescale or denormalize the predictions on your own without using this tool. - Also, if the best min and max for the use case is 0 and 1, then no rescaling is needed since that is already the scale of the predictions. - If a user requests to convert from probabilities to a unit in levels, be sure to caveat your use of this tool by noting that probabilities do not always scale uniformly to levels. Rescaling can be used as a rough first-pass estimate. But for best results, it would be better to start a new Chronulus forecasting use case predicting in levels from the start. How to use this tool: - To use this tool present prediction_id from the normalized prediction and the min and max as floats - If the user is also changing units, consider if the units will be inverted and set the inverse scale to True if needed. - When plotting the rescaled predictions, use a Rechart time series plot with the appropriate axes labeled and include the chronulus prediction explanation as a caption below the plot. - If you would like to add additional notes about the scaled series, put these below the original prediction explanation.
A tool that saves a Chronulus forecast from NormalizedForecaster to separate CSV and TXT files When to use this tool: - Use this tool when you need to save both the forecast data and its explanation to files - The forecast data will be saved as a CSV file for data analysis - The forecast explanation will be saved as a TXT file for reference - Both files will be saved in the same directory specified by output_path - This tool can also be used to directly save rescaled predictions without first calling the rescaling tool How to use this tool: - Provide the prediction_id from a previous forecast - Specify the output_path where both files should be saved - Provide csv_name for the forecast data file (must end in .csv) - Provide txt_name for the explanation file (must end in .txt) - Optionally provide y_min and y_max to rescale the predictions (defaults to 0) - Set invert_scale to True if the target units run in the opposite direction - The tool will provide status updates through the MCP context
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