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Chronulus MCP Server

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reuse_forecasting_agent_and_get_forecast

Generate forecasts for values between 0 and 1 using a NormalizedForecaster agent. Input session data, provide relevant features, and specify forecast horizon and time scale to receive predictions and explanations. Ideal for seasonal weights, probabilities, or share forecasts without historical data.

Instructions

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

Input Schema

NameRequiredDescriptionDefault
agent_idYesThe agent_id for the forecasting or prediction use case and previously defined input_data_model
forecast_start_dt_strYesThe datetime str in '%Y-%m-%d %H:%M:%S' format of the first value in the forecast horizon.
horizon_lenNoThe integer length of the forecast horizon. Eg., 60 if a 60 day forecast was requested.
input_dataYesThe forecast inputs that you will pass to the chronulus agent to make the prediction. The keys of the dict should correspond to the InputField name you provided in input_fields.
time_scaleNoThe times scale of the forecast horizon. Valid time scales are 'hours', 'days', and 'weeks'.days

Input Schema (JSON Schema)

{ "properties": { "agent_id": { "description": "The agent_id for the forecasting or prediction use case and previously defined input_data_model", "title": "Agent Id", "type": "string" }, "forecast_start_dt_str": { "description": "The datetime str in '%Y-%m-%d %H:%M:%S' format of the first value in the forecast horizon.", "title": "Forecast Start Dt Str", "type": "string" }, "horizon_len": { "default": 60, "description": "The integer length of the forecast horizon. Eg., 60 if a 60 day forecast was requested.", "title": "Horizon Len", "type": "integer" }, "input_data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "type": "object" }, { "items": { "type": "object" }, "type": "array" } ] }, "description": "The forecast inputs that you will pass to the chronulus agent to make the prediction. The keys of the dict should correspond to the InputField name you provided in input_fields.", "title": "Input Data", "type": "object" }, "time_scale": { "default": "days", "description": "The times scale of the forecast horizon. Valid time scales are 'hours', 'days', and 'weeks'.", "title": "Time Scale", "type": "string" } }, "required": [ "agent_id", "input_data", "forecast_start_dt_str" ], "title": "reuse_forecasting_agent_and_get_forecastArguments", "type": "object" }

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