forecasting-mcp

bayesmaxxing/forecasting-mcp

3.1

If you are the rightful owner of forecasting-mcp and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

This is a Model Context Protocol (MCP) implementation for interacting with a forecasting website, enabling LLMs to manage forecasts and access external information.

Tools
5
Resources
0
Prompts
0

Forecasting MCP

This is a Model Context Protocol (MCP) implementation for interacting with a forecasting website. It enables LLMs to make, update, and delete forecasts, as well as retrieve forecast data and access external information through Perplexity API.

Features

  • Make forecasts on an existing forecasting website
  • Update existing forecasts
  • Get information about forecasts
  • Access Perplexity AI for additional context and research

Setup

  1. Install the required dependencies:
pip install .
  1. Create a .env file with the following variables:
API_URL=<your_forecasting_api_url>
BOT_USERNAME=<your_bot_username>
BOT_PASSWORD=<your_bot_password>
PERPLEXITY_API_KEY=<your_perplexity_api_key>  # For accessing Perplexity API

Usage

Run the server with:

uv run forecasting.py

Available MCP Tools

Forecasts Management

  • get_forecasts: List forecasts by category and status
  • get_forecast_data: Get details for a specific forecast
  • update_forecast: Create a new forecast point for a forecast.

Forecast Points

  • get_forecast_points: Get all forecast points for a forecast

External Information

  • query_perplexity: Query the Perplexity API for additional information to inform forecasts

Dependencies

  • Python 3.13+
  • FastAPI
  • httpx
  • MCP
  • requests

Development

This server uses FastAPI and the MCP library to provide a standardized interface for LLMs to interact with forecasting data.