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Chronulus AI Forecasting

Organization
chronulusai

MCP Server for Chronulus AI Forecasting and Prediction Agents

Publisherchronulusai
Repositorychronulus-mcp
LanguagePython
Forks
20
Stars
108
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Chronulus AI Forecasting exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    108 stars and 20 forks from the linked repository.

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

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": "<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.

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": "<YOUR_CHRONULUS_API_KEY>"
      }
    }
  }
}

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": "<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:

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.

Installation

TypingMind
Prerequisites:

Node.js 18+

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

Use Chronulus AI Forecasting MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Chronulus AI Forecasting is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Chronulus AI Forecasting as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Chronulus AI Forecasting MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "chronulus-ai-forecasting": {
      "command": "npx",
      "args": [
        "-y",
        "chronulus-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Chronulus AI Forecasting MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Chronulus AI Forecasting MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Chronulus AI Forecasting to help me with this task?
Chronulus AI Forecasting
Sure. I read it.
Here is what I found using Chronulus AI Forecasting.

Frequently asked questions

What is the Chronulus AI Forecasting MCP server used for?

Chronulus AI Forecasting is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Chronulus AI Forecasting MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Chronulus AI Forecasting with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Chronulus AI Forecasting MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Chronulus AI Forecasting connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Chronulus AI Forecasting MCP to TypingMind?

Chronulus AI Forecasting runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Chronulus AI Forecasting MCP provide in TypingMind?

Chronulus AI Forecasting exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Chronulus AI Forecasting MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Chronulus AI Forecasting requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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