Which integrations are available for this server?

Generates ready-to-render Chart.js configurations for visualizing time-series forecasts, featuring historical data points, predicted trends, and confidence intervals.

How do I use Prophet MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Prophet MCP Server Forecast the next 7 days of sales based on this historical data." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Prophet MCP Server

An open-source Model Context Protocol (MCP) server engineered for Time-Series Forecasting.

Powered by Meta's Prophet, this server enables LLMs to generate accurate forecasts, trend analyses, and confidence intervals from historical data — turning raw numbers into actionable insights within AI workflows.

Note: This project is a specialized fork of the sendgrid-mcp server, re-engineered to provide robust forecasting capabilities via the MCP protocol.

🚀 Key Capabilities

1. Predictive Modeling

Leverages Meta's Prophet to predict future trends based on historical data. Handles seasonality, outliers, and trend changes automatically.

2. LLM-Friendly Output

Returns data in a format optimized for Large Language Models:

Plain-English Summaries: Instant context on trends (e.g., "Trending UPWARD by +51.7%").
Statistical Breakdowns: Historical vs. Forecasted means, min/max, standard deviations.
Chart.js Config: Ready-to-render visualization config for web deployment.

3. Bounds Validation

Optional upper/lower limits to flag out-of-bounds forecasts — turning predictions into decision-support with business-rule enforcement.

4. Interactive Visualization

Includes Chart.js configuration in every response with:

Red dots for actual historical data
Dashed blue line for forecast predictions
Shaded confidence interval band
Red/orange dashed limit lines (when bounds are set)

📖 How It Works

┌─────────────────────────────────────────────────────────────┐ │ 1. LLM sends your historical data (dates + values) │ │ 2. Prophet model learns the pattern │ │ 3. Server generates forecast for N future periods │ │ 4. Response includes: │ │ ├── Human-readable summary with trend analysis │ │ ├── Forecast data table (with optional bounds status) │ │ └── Chart.js config for instant visualization │ └─────────────────────────────────────────────────────────────┘

📊 Real-World Example

Let's say you tracked daily website conversions over 10 days and want to forecast the next 5 days — with a safety limit of max 22 conversions (your team can't handle more).

Input

{ "ds": ["2025-01-01", "2025-01-02", "2025-01-03", "2025-01-04", "2025-01-05", "2025-01-06", "2025-01-07", "2025-01-08", "2025-01-09", "2025-01-10"], "y": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], "periods": 5, "upper_limit": 22 }

Output

### Prophet Forecast Data ### Summary of forecast metrics: - Historical Period: 2025-01-01 to 2025-01-10 - Historical Data Points: 10 - Historical Mean: 14.50 - Forecast Periods: 5 - Trend Direction: UPWARD (+51.7% vs historical mean) Bounds Validation: - Upper Limit: 22 - ⚠️ 2 date(s) OUT OF BOUNDS: 2025-01-14: yhat=23.00 > upper_limit=22 2025-01-15: yhat=24.00 > upper_limit=22 Key Takeaway: The model predicts the values will trend upward over the next 5 periods, with predicted values ranging from 20.00 to 24.00. Date | yhat | yhat_lower | yhat_upper | Status ---------------------------------------------------- 2025-01-01 | 10.00 | 10.00 | 10.00 | ✅ OK ... 2025-01-14 | 23.00 | 23.00 | 23.00 | ⚠️ EXCEEDS UPPER 2025-01-15 | 24.00 | 24.00 | 24.00 | ⚠️ EXCEEDS UPPER chartjs = { ... }

No data-science expertise required. The output tells you the trend direction, flags risky dates, and provides visualization config — all in plain text.

🛠️ Tool: `forecast_time_series`

Description

Ingests time-series data and returns a future forecast with a detailed text summary, bounds validation, and Chart.js visualization config.

Input Parameters

Parameter	Type	Required	Default	Description
`ds`	`array[string]`	✅ Yes	—	List of dates in ISO format (YYYY-MM-DD)
`y`	`array[number]`	✅ Yes	—	List of numeric values aligned with `ds`
`periods`	`integer`	No	`10`	Number of future periods to forecast
`lower_limit`	`number`	No	—	Flag forecast values below this threshold
`upper_limit`	`number`	No	—	Flag forecast values above this threshold

Output Columns

Column	Meaning
`ds`	Date for the observed or predicted value
`yhat`	Predicted value (model's best estimate)
`yhat_lower`	Lower bound of confidence interval (worst-case)
`yhat_upper`	Upper bound of confidence interval (best-case)
`status`	✅ OK, ⚠️ EXCEEDS UPPER, or ⚠️ BELOW LOWER (only when limits are set)

Understanding the Two Types of Bounds

	`yhat_lower` / `yhat_upper`	`lower_limit` / `upper_limit`
Set by	Prophet model (automatic)	You (manual)
Purpose	Statistical confidence range	Business rule enforcement
Answers	"How sure is the model?"	"Is the forecast safe for my business?"
Example	"Revenue will be 800–1200"	"Our warehouse can't handle > 1000 orders"

📂 Project Structure

Prophet_mcp/ ├── app.py # Flask server — MCP endpoint, auth, JSON-RPC routing ├── mcp_helper.py # Core engine — Prophet forecasting, summary, Chart.js config ├── requirements.txt # Python dependencies ├── README.md # This file ├── .gitignore # Git exclusions └── examples/ # Local testing utilities (not required for deployment) ├── plot_forecast.py # Script to call API and generate a local HTML chart └── forecast_chart.html # Auto-generated preview (gitignored)

📦 Installation & Setup

Prerequisites

Anaconda or Miniconda (recommended for Prophet dependencies)
Python 3.11+

1. Environment Setup

# Create environment conda create -n prophet-mcp python=3.11 conda activate prophet-mcp # Install dependencies pip install -r requirements.txt

Windows Users: Prophet requires CmdStan. If you encounter issues, refer to the Prophet Installation Guide or install via conda: conda install -c conda-forge prophet.

2. Configuration

The server uses Bearer Token authentication. Set the MCP_TOKEN environment variable, or it defaults to the value in app.py:

# Set your token (recommended for production) export MCP_TOKEN="your-secure-token-here"

🏃‍♂️ Running the Server

python app.py

Server URL: http://localhost:3000
MCP Endpoint: POST http://localhost:3000/mcp

Authentication

All requests must include the header:

Authorization: Bearer <your-token>

Example API Call (cURL)

curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -H "Authorization: `MCP_TOKEN` \ -d '{ "jsonrpc": "2.0", "method": "tools/call", "params": { "name": "forecast_time_series", "arguments": { "ds": ["2025-01-01","2025-01-02","2025-01-03","2025-01-04","2025-01-05", "2025-01-06","2025-01-07","2025-01-08","2025-01-09","2025-01-10"], "y": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], "periods": 5, "upper_limit": 22 } }, "id": 1 }'

🧪 Testing & Visualization

Local Testing Script

python examples/plot_forecast.py

This script will:

Call your MCP server's API
Extract the Chart.js config from the response
Generate forecast_chart.html with an interactive chart
Open it in your default browser

The generated chart features a dark glassmorphism theme with:

🔴 Red dots — Historical actuals
🔵 Dashed blue line — Forecast predictions
🟦 Shaded blue band — Confidence interval
🔴 Red dashed line — Upper limit (if set)
🟠 Orange dashed line — Lower limit (if set)

☁️ Cloud Deployment

For deploying to Google Cloud (or any cloud provider), you only need:

app.py mcp_helper.py requirements.txt

The examples/ folder is for local testing only and is not required in production.

🔐 Security

Bearer Token authentication on all endpoints
Token configurable via MCP_TOKEN environment variable
JSON-RPC error handling with proper error codes
Input validation on all tool parameters

📄 Dependencies

Package	Purpose
`flask`	Web server framework
`pandas`	Data manipulation
`prophet`	Time-series forecasting engine
`requests`	HTTP client (examples only)

📄 License

MIT License

👥 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Author: Pradeep Chandra Kalahasthi
Original Base: sendgrid-mcp

Prophet MCP Server

Prophet MCP Server

🚀 Key Capabilities

1. Predictive Modeling

2. LLM-Friendly Output

3. Bounds Validation

4. Interactive Visualization

📖 How It Works

📊 Real-World Example

Input

Output

🛠️ Tool: `forecast_time_series`

Description

Input Parameters

Output Columns

Understanding the Two Types of Bounds

📂 Project Structure

📦 Installation & Setup

Prerequisites

1. Environment Setup

2. Configuration

🏃‍♂️ Running the Server

Authentication

Example API Call (cURL)

🧪 Testing & Visualization

Local Testing Script

☁️ Cloud Deployment

🔐 Security

📄 Dependencies

📄 License

👥 Contributing

Resources

Latest Blog Posts

MCP directory API

Prophet MCP Server

🚀 Key Capabilities

1. Predictive Modeling

2. LLM-Friendly Output

3. Bounds Validation

4. Interactive Visualization

📖 How It Works

📊 Real-World Example

Input

Output

🛠️ Tool: forecast_time_series

Description

Input Parameters

Output Columns

Understanding the Two Types of Bounds

📂 Project Structure

📦 Installation & Setup

Prerequisites

1. Environment Setup

2. Configuration

🏃‍♂️ Running the Server

Authentication

Example API Call (cURL)

🧪 Testing & Visualization

Local Testing Script

☁️ Cloud Deployment

🔐 Security

📄 Dependencies

📄 License

👥 Contributing

Resources

Latest Blog Posts

MCP directory API

🛠️ Tool: `forecast_time_series`