How to add Chronulus AI MCP Server to Cursor

Chat with Chronulus AI forecasting and prediction agents directly from Claude. Paste the config into ~/.cursor/mcp.json and restart Cursor.

Last updated June 14, 2026 · 111★ · stdio · apikey · official

Cursor config for Chronulus AI MCP Server

pip install chronulus-mcp

{
  "mcpServers": {
    "chronulus-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "chronulus-mcp"
      ],
      "env": {
        "CHRONULUS_API_KEY": "<YOUR_CHRONULUS_API_KEY>"
      }
    }
  }
}

Requires `uv` (the Python package runner). Install it from https://docs.astral.sh/uv/ if `uvx` is not found.

Setup steps

1Open Cursor → Settings → MCP → Add new MCP server (or edit ~/.cursor/mcp.json directly).
2Paste the Chronulus AI MCP Server config below into the "mcpServers" object.
3Fill in placeholder secrets, then save.
4Cursor reloads MCP servers automatically — check Settings → MCP for a green status dot.
5Ask Cursor to use one of Chronulus AI MCP Server's tools to confirm it's connected.

Before you start

A Chronulus API key (CHRONULUS_API_KEY)
Python 3.10+ (when installed via pip), or uv/uvx, or Docker
An MCP client such as Claude for Desktop

What Chronulus AI MCP Server can do in Cursor

create_chronulus_session

Creates a new Chronulus session from a situation and task describing the forecasting/prediction use case and returns a reusable session_id.

create_forecasting_agent_and_get_forecast

Creates a NormalizedForecaster agent with a session and input data model, submits forecast input data, and returns prediction data (values between 0 and 1) plus a text explanation.

reuse_forecasting_agent_and_get_forecast

Submits new forecast input to a previously created NormalizedForecaster agent (unchanged input data model) and returns prediction data and a text explanation.

rescale_forecast

Rescales NormalizedForecaster predictions (values between 0 and 1) to a use-case-specific min/max, with optional inverse scaling.

save_forecast

Saves a NormalizedForecaster forecast to separate CSV (data) and TXT (explanation) files, with optional rescaling via y_min/y_max and invert_scale.

create_prediction_agent_and_get_predictions

Creates a BinaryPredictor agent with a session and input data model, submits prediction input, and returns a consensus probability from a panel of experts plus individual estimates, explanations, and Beta-distribution alpha/beta parameters.

reuse_prediction_agent_and_get_prediction

Submits new prediction input to a previously created BinaryPredictor agent (unchanged input data model) and returns the consensus probability, expert estimates, explanations, and Beta-distribution parameters.

save_prediction_analysis_html

Saves a BinaryPredictor prediction analysis to an HTML file, including a plot of the theoretical and empirical Beta distribution and the experts' opinions.

Security

Requires a Chronulus API key supplied via the CHRONULUS_API_KEY environment variable. Inputs (text and files) are sent to the Chronulus AI cloud platform for processing; total input size cannot exceed 10MB.

Chronulus AI MCP Server + Cursor FAQ

Where is the Cursor config file?

Cursor reads MCP servers from ~/.cursor/mcp.json. Paste the Chronulus AI MCP Server config there under the "mcpServers" key and restart the client.

Is Chronulus AI MCP Server safe to use with Cursor?

How do I authenticate with Chronulus?

Set the CHRONULUS_API_KEY environment variable in your MCP client config to your Chronulus API key.

What input formats can I pass to the agents?

You can pass plain text or files by path, including images, text files, and PDFs (via input types such as ImageFromFile, TextFromFile, and PdfFromFile). The total size of all inputs cannot exceed 10MB.

Do I need historical data to forecast?

No. Both the NormalizedForecaster and BinaryPredictor agents are designed to produce forecasts and probability estimates without requiring historical data.

View repo Full Chronulus AI MCP Server page