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ChronulusAI/chronulus-mcp

by Various

MCP Server for Chronulus AI Forecasting and Prediction Agents

C

MCP

ChronulusAI/chronulus-mcp

Added 1 June 2026

Overview

Chronulus-mcp is a Model Context Protocol server that exposes Chronulus AI forecasting and prediction agents as callable tools. It allows developers to integrate probabilistic predictions into LLM workflows by defining prediction tasks and retrieving forecast results through a standardized MCP interface.

Best for

Best for
Developers building AI agents or assistants that need structured probabilistic forecasts

Use cases

  • Add probabilistic forecasting to AI assistants for business or market predictions
  • Automate scenario analysis by querying prediction agents from chat interfaces
  • Build decision-support tools that combine LLM reasoning with structured forecasts

How to use

Install

pip install chronulus-mcp

Tools exposed

  • python
  • docker
  • uvx
  • npx

Tested with

Claude Desktop

Example client config

{\n  "mcpServers": {\n    "chronulus-agents": {\n      "command": "python",\n      "args": ["-m", "chronulus_mcp"],\n      "env": {\n        "CHRONULUS_API_KEY": "<YOUR_CHRONULUS_API_KEY>"\n      }\n    }\n  }\n}

Notes

Chronulus-mcp is a Model Context Protocol server that exposes Chronulus AI forecasting and prediction agents as callable tools. It allows developers to integrate probabilistic predictions into LLM workflows by defining prediction tasks and retrieving forecast results through a standardized MCP interface.

108 stars on GitHub. Last updated 2025-07-19. Licensed MIT.

Use cases

  • Add probabilistic forecasting to AI assistants for business or market predictions
  • Automate scenario analysis by querying prediction agents from chat interfaces
  • Build decision-support tools that combine LLM reasoning with structured forecasts

Pros

  • Provides a clean MCP interface for integrating specialized forecasting models
  • Written in Python, easy to extend or embed in existing Python projects
  • Active development with 108 GitHub stars indicates community interest

Cons

  • Requires understanding of both MCP protocol and Chronulus AI API
  • Limited to forecasting tasks; not a general-purpose tool
  • Dependency on external Chronulus AI service for predictions

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Provides a clean MCP interface for integrating specialized forecasting models
  • Written in Python, easy to extend or embed in existing Python projects
  • Active development with 108 GitHub stars indicates community interest

Cons

  • Requires understanding of both MCP protocol and Chronulus AI API
  • Limited to forecasting tasks; not a general-purpose tool
  • Dependency on external Chronulus AI service for predictions
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