Chronulus AI
officialPredict anything with Chronulus AI forecasting and prediction agents.
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:
- install uv or
- Provide the absolute path to
uvx. For example/Users/username/.local/bin/uvxinstead 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:
{
"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.
Related Servers
Alpha Vantage MCP Server
sponsorAccess financial market data: realtime & historical stock, ETF, options, forex, crypto, commodities, fundamentals, technical indicators, & more
GitLab MR & Confluence Linker
Analyzes GitLab merge requests and links them to Confluence documentation.
Pinelabs MCP Server
The Pine Labs Online MCP Server implements the Model Context Protocol (MCP) to enable seamless integration between Pine Labs’ online payment APIs and AI tools. It allows AI assistants to perform Pine Labs Online API operations, empowering developers to build intelligent, AI-driven payment applications with ease.
A2ABench
Agent-native developer Q&A API with MCP + A2A endpoints for citations, job pickup, and answer submission.
MCP Server Starter
A TypeScript starter project for building Model Context Protocol (MCP) servers with Bun.
Remote MCP Server (Authless)
An example of a remote MCP server deployable on Cloudflare Workers without authentication.
Godot MCP Pro
Premium MCP server for Godot game engine with 84 AI-powered tools for scene editing, scripting, animation, tilemap, shader, input simulation, and runtime debugging.
Playwright MCP
Generate Playwright tests with AI assistants by providing real-time access to the browser DOM, interactions, and screenshots.
Hyperlend
Enables AI agents to interact with the Hyperlend protocol.
Bifrost
Exposes VSCode's development tools and language features to AI tools through an MCP server.
Cloudflare MCP Server Example
An example of deploying a remote MCP server on Cloudflare Workers without authentication.