The Chronulus MCP Server enables interaction with AI Forecasting & Prediction Agents through the Model Context Protocol, allowing users to:
- Create Sessions: Define new forecasting or prediction use cases
- Generate Forecasts: Create normalized forecasts (0-1) via
create_forecasting_agent_and_get_forecast
andreuse_forecasting_agent_and_get_forecast
- Rescale Forecasts: Adjust predictions to specific ranges
- Binary Predictions: Estimate probabilities for binary outcomes using BinaryPredictor agents
- Risk Assessment: Retrieve risk assessment scorecards in Markdown/JSON
- Save Outputs: Store forecasts (CSV/TXT) and prediction analysis (HTML)
- Input Support: Work with diverse inputs including text, images, PDFs, and files
- Deployment Options: Multiple installation methods (pip, Docker, uv) with third-party server integration
Supports containerized deployment of the Chronulus MCP server using Docker images
Provides access to the Chronulus MCP source code repository for installation and contribution
Compatible with Claude for Desktop on macOS for forecasting and prediction capabilities
Allows installation of the Chronulus MCP server package directly from the Python Package Index
Quickstart: Claude for Desktop
Install
Claude for Desktop is currently available on macOS and Windows.
Install Claude for Desktop here
Configuration
Follow the general instructions here to configure the Claude desktop client.
You can find your Claude config at one of the following locations:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json
- Windows:
%APPDATA%\Claude\claude_desktop_config.json
Then choose one of the following methods that best suits your needs and add it to your claude_desktop_config.json
(Option 1) Install release from PyPI
(Option 2) Install from Github
Note, if you get an error like "MCP chronulus-agents: spawn python ENOENT",
then you most likely need to provide the absolute path to python
.
For example /Library/Frameworks/Python.framework/Versions/3.11/bin/python3
instead of just python
Here we will build a docker image called 'chronulus-mcp' that we can reuse in our Claude config.
In your Claude config, be sure that the final argument matches the name you give to the docker image in the build command.
uvx
will pull the latest version of chronulus-mcp
from the PyPI registry, install it, and then run it.
Note, if you get an error like "MCP chronulus-agents: spawn uvx ENOENT", then you most likely need to either:
- install uv or
- Provide the absolute path to
uvx
. For example/Users/username/.local/bin/uvx
instead of justuvx
Additional Servers (Filesystem, Fetch, etc)
In our demo, we use third-party servers like fetch and filesystem.
For details on installing and configure third-party server, please reference the documentation provided by the server maintainer.
Below is an example of how to configure filesystem and fetch alongside Chronulus in your claude_desktop_config.json
:
Claude Preferences
To streamline your experience using Claude across multiple sets of tools, it is best to add your preferences to under Claude Settings.
You can upgrade your Claude preferences in a couple ways:
- From Claude Desktop:
Settings -> General -> Claude Settings -> Profile (tab)
- From claude.ai/settings:
Profile (tab)
Preferences are shared across both Claude for Desktop and Claude.ai (the web interface). So your instruction need to work across both experiences.
Below are the preferences we used to achieve the results shown in our demos:
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Tools
Enables integration of Chronulus AI Forecasting & Prediction Agents with Claude, allowing users to access AI-powered forecasting and prediction capabilities directly through AI clients that support MCP.
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