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MCP Server for Chronulus: AI Forecasting & Real-Time Analytics - MCP Implementation

MCP Server for Chronulus: AI Forecasting & Real-Time Analytics

Empower data-driven decisions with MCP Server for Chronulus—AI forecasting agents deliver real-time predictions with unparalleled precision, unlocking strategic advantages for your business.

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About MCP Server for Chronulus

What is MCP Server for Chronulus: AI Forecasting & Real-Time Analytics?

MCP Server integrates Chronulus' AI forecasting and real-time analytics capabilities into the Claude ecosystem, enabling users to leverage predictive models and data-driven insights directly within the Claude desktop environment. It serves as a bridge between advanced analytical tools and seamless workflow integration, supporting cross-platform operation and flexible deployment methods.

How to Use MCP Server for Chronulus: AI Forecasting & Real-Time Analytics?

  1. Install Claude Desktop and configure API access.
  2. Choose your preferred installation method (pip, Docker, or uvx) to deploy the MCP Server core components.
  3. Edit the settings.json file to define server configurations, including API keys and workspace paths.
  4. Launch servers for Chronulus, filesystem access, and web resource fetching via command-line instructions.
  5. Use Claude's interface to trigger analytical workflows, visualize forecasts, and manage files through standardized tool protocols.

MCP Server for Chronulus Features

Key Features of MCP Server for Chronulus: AI Forecasting & Real-Time Analytics?

  • Multi-installation flexibility: Deploy via package managers (pip/uvx), containerization (Docker), or npm for rapid setup.
  • Modular server architecture: Combine Chronulus analytics with filesystem and web scraping modules for end-to-end data pipelines.
  • Security-first design: Environment variable isolation for API credentials and granular access controls.
  • Cross-platform compatibility: Full functionality on macOS, Linux, and Windows environments.

Use Cases of MCP Server for Chronulus: AI Forecasting & Real-Time Analytics?

Typical applications include:

  • Financial institutions for market trend prediction and risk modeling
  • Operations teams monitoring real-time sensor data streams
  • Marketing analysts tracking social media sentiment shifts
  • Content creators generating data-backed reports with auto-populated explanations

MCP Server for Chronulus FAQ

FAQ from MCP Server for Chronulus: AI Forecasting & Real-Time Analytics?

What Python version is required?
Python 3.8+ recommended for compatibility with all dependency packages.
Why use uvx over pip?
uvx provides one-step installation with dependency resolution, ideal for quick prototyping.
Can I disable the filesystem server?
Yes, but disables file-based input/output operations critical for many Chronulus workflows.
How to resolve "command not found" errors?
Ensure node.js and docker are installed system-wide, and check $PATH configurations.
Where can I learn more about API key management?
Visit the official documentation for security best practices.

Content

Chronulus AI

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

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

Using docker

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>"
      }
    }
  }
}
Using uvx

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:

  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": "<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.

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