Chronulus MCP Server

What is Chronulus MCP Server about? Enables Claude to interact with Chronulus AI forecasting and prediction agents, allowing users to ask questions, run forecasts, and retrieve insights directly within the Claude chat environment.

How to use Chronulus MCP Server?

Install the server (pip, Docker, or uvx) and obtain a Chronulus API key.
Add a server entry to claude_desktop_config.json pointing to the installed server.
Configure the CHRONULUS_API_KEY environment variable in the entry.
Restart Claude Desktop; the new "chronulus‑agents" server will appear as a tool.

Key features of Chronulus MCP Server

Direct access to Chronulus forecasting, prediction, and explanation tools.
Supports text, PDF, and image inputs via specialized field types.
Works on macOS and Windows through Claude for Desktop.
Multiple deployment options: pip install, Docker image, or uvx.
Configurable via simple JSON in Claude’s settings.

Use cases of Chronulus MCP Server

Generating financial or sales forecasts within a conversational workflow.
Asking predictive questions about time‑series data without leaving Claude.
Visualizing forecasted trends with automatically attached explanations.
Integrating Chronulus insights with other MCP tools such as filesystem or fetch.

FAQ

Do I need a Chronulus account? Yes, an active API key from Chronulus is required.
Which operating systems are supported? macOS and Windows (Claude for Desktop).
Can I run the server in Docker? Absolutely – build the image with docker build . -t chronulus-mcp and reference it in the config.
What if I get a "spawn python ENOENT" error? Provide the absolute path to the Python executable in the command.
Is there a Node.js version? No, the server is a Python package; use the provided Python command.

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

pip install chronulus-mcp

(Option 2) Install from Github

git clone https://github.com/ChronulusAI/chronulus-mcp.git
cd chronulus-mcp
pip install .

{
  "mcpServers": {
    "chronulus-agents": {
      "command": "python",
      "args": ["-m", "chronulus_mcp"],
      "env": {
        "CHRONULUS_API_KEY": "<YOUR_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

Here we will build a docker image called 'chronulus-mcp' that we can reuse in our Claude config.

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.

{
  "mcpServers": {
    "chronulus-agents": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "CHRONULUS_API_KEY", "chronulus-mcp"],
      "env": {
        "CHRONULUS_API_KEY": "<YOUR_CHRONULUS_API_KEY>"
      }
    }
  }
}

uvx will pull the latest version of chronulus-mcp from the PyPI registry, install it, and then run it.

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

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 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": "<YOUR_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: 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.

Chronulus MCP Server