What is Chronulus AI?

Chronulus AI is a forecasting and prediction agents platform that integrates with Claude to provide advanced analytical capabilities.

How to use Chronulus AI?

To use Chronulus AI, you need to:

1. Install Claude for Desktop
2. Configure the Claude desktop client
3. Add Chronulus AI to your MCP servers
4. Set up preferences in Claude to optimize the use of Chronulus AI tools

Key features of Chronulus AI?

• AI forecasting and prediction agents - Advanced AI models for accurate predictions
• Multiple installation methods - Support for pip, docker, and uvx
• Integration with Claude - Seamless interaction with Claude for enhanced functionality
• Customizable configurations - Ability to add additional servers like filesystem and fetch

Use cases of Chronulus AI?

1. Business forecasting for sales and market trends
2. Predictive analytics for financial markets
3. Time series analysis for various industries
4. Integration with other tools to enhance data analysis capabilities

FAQ from Chronulus AI?

• Is Chronulus AI free to use?

No, Chronulus AI requires an API key for usage.

• What platforms does Claude for Desktop support?

Claude for Desktop is currently available on macOS and Windows.

• How do I configure Chronulus AI in Claude?

Follow the general instructions provided in the quickstart guide and add the configuration to your claude_desktop_config.json under MCP servers.

• What are the system requirements for running Chronulus AI?

The system requirements depend on the installation method chosen (pip, docker, or uvx). Make sure you have the necessary dependencies installed.

MCP Server for Chronulus

Chat with Chronulus AI Forecasting & Prediction Agents in Claude

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

Using pip (Option 1) Install release from PyPI bash pip install chronulus-mcp (Option 2) Install from Github bash git clone https://github.com/ChronulusAI/chronulus-mcp.git cd chronulus-mcp pip install . json { "mcpServers": { "chronulus-agents": { "command": "python", "args": ["-m", "chronulus_mcp"], "env": { "CHRONULUS_API_KEY": "" } } } } 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

Using docker Here we will build a docker image called 'chronulus-mcp' that we can reuse in our Claude config. bash git clone https://github.com/ChronulusAI/chronulus-mcp.git cd chronulus-mcp docker build . -t 'chronulus-mcp' In your Claude config, be sure that the final argument matches the name you give to the docker image in the build command. json { "mcpServers": { "chronulus-agents": { "command": "docker", "args": ["run", "-i", "--rm", "-e", "CHRONULUS_API_KEY", "chronulus-mcp"], "env": { "CHRONULUS_API_KEY": "" } } } }

Using uvx uvx will pull the latest version of chronulus-mcp from the PyPI registry, install it, and then run it. json { "mcpServers": { "chronulus-agents": { "command": "uvx", "args": ["chronulus-mcp"], "env": { "CHRONULUS_API_KEY": "" } } } } Note, if you get an error like "MCP chronulus-agents: spawn uvx ENOENT", then you most likely need to either: 1. install uv or 2. Provide the absolute path to uvx. For example /Users/username/.local/bin/uvx instead of just uvx

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:

{
  "mcpServers": {
    "chronulus-agents": {
      "command": "uvx",
      "args": ["chronulus-mcp"],
      "env": {
        "CHRONULUS_API_KEY": "" } }, "filesystem": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-filesystem", "/path/to/AIWorkspace" ] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } } ``` #### 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](https://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: ``` ## Tools-Dependent Protocols The following instructions apply only when tools/MCP Servers are accessible. ### Filesystem - Tool Instructions - Do not use 'read_file' or 'read_multiple_files' on binary files (e.g., images, pdfs, docx) . - When working with binary files (e.g., images, pdfs, docx) use 'get_info' instead of 'read_*' tools to inspect a file. ### Chronulus Agents - Tool Instructions - When using Chronulus, prefer to use input field types like TextFromFile, PdfFromFile, and ImageFromFile over scanning the files directly. - When plotting forecasts from Chronulus, always include the Chronulus-provided forecast explanation below the plot and label it as Chronulus Explanation. ```