create_forecasting_agent_and_get_forecast

Create a forecasting agent, provide input data, and retrieve normalized predictions (values between 0 and 1) with text explanations. Ideal for modeling seasonal weights, probabilities, or shares without historical data. Use session ID, input model, and forecast parameters for accurate results.

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

Name	Required	Description	Default
`forecast_start_dt_str`	Yes	The datetime str in '%Y-%m-%d %H:%M:%S' format of the first value in the forecast horizon.
`horizon_len`	No	The integer length of the forecast horizon. Eg., 60 if a 60 day forecast was requested.
`input_data`	Yes	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.
`input_data_model`	Yes	Metadata on the fields you will include in the input_data.
`session_id`	Yes	The session_id for the forecasting or prediction use case
`time_scale`	No	The times scale of the forecast horizon. Valid time scales are 'hours', 'days', and 'weeks'.	days

Input Schema (JSON Schema)

{
  "$defs": {
    "InputField": {
      "properties": {
        "description": {
          "description": "A description of the value you will pass in the field.",
          "title": "Description",
          "type": "string"
        },
        "name": {
          "description": "Field name. Should be a valid python variable name.",
          "title": "Name",
          "type": "string"
        },
        "type": {
          "default": "str",
          "description": "The type of the field. \n        ImageFromFile takes a single named-argument, 'file_path' as input which should be absolute path to the image to be included. So you should provide this input as json, eg. {'file_path': '/path/to/image'}.\n        ",
          "enum": [
            "str",
            "Text",
            "List[Text]",
            "TextFromFile",
            "List[TextFromFile]",
            "PdfFromFile",
            "List[PdfFromFile]",
            "ImageFromFile",
            "List[ImageFromFile]"
          ],
          "title": "Type",
          "type": "string"
        }
      },
      "required": [
        "name",
        "description"
      ],
      "title": "InputField",
      "type": "object"
    }
  },
  "properties": {
    "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"
    },
    "input_data_model": {
      "description": "Metadata on the fields you will include in the input_data.",
      "items": {
        "$ref": "#/$defs/InputField"
      },
      "title": "Input Data Model",
      "type": "array"
    },
    "session_id": {
      "description": "The session_id for the forecasting or prediction use case",
      "title": "Session Id",
      "type": "string"
    },
    "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": [
    "session_id",
    "input_data_model",
    "input_data",
    "forecast_start_dt_str"
  ],
  "title": "create_forecasting_agent_and_get_forecastArguments",
  "type": "object"
}

Chronulus MCP Server