MCP Analytics

🚀 MCP Analytics Suite

MCP Analytics Suite is an intelligent analytics platform that automatically selects the right analytical approach based on your business questions, eliminating the need for a statistics degree.

🚀 Quick Start

Installation

You can install MCP Analytics in different IDEs as follows:

For Claude Desktop

Add the following content to your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "mcp-analytics": {
      "command": "npx",
      "args": ["-y", "mcp-remote@latest", "https://api.mcpanalytics.ai/auth0"]
    }
  }
}

For Cursor

Add the following content to .cursor/config.json in your project root:

{
  "mcpServers": {
    "mcp-analytics": {
      "command": "npx",
      "args": ["-y", "mcp-remote@latest", "https://api.mcpanalytics.ai/auth0"]
    }
  }
}

For VS Code (Continue Extension)

Add the following content to your Continue config at ~/.continue/config.json:

{
  "models": [{
    "provider": "anthropic",
    "model": "claude-3-5-sonnet",
    "mcpServers": {
      "mcp-analytics": {
        "command": "npx",
        "args": ["-y", "mcp-remote@latest", "https://api.mcpanalytics.ai/auth0"]
      }
    }
  }]
}

For Claude Code

Add the following content to claude_code_config.json:

{
  "mcpServers": {
    "mcp-analytics": {
      "command": "npx",
      "args": ["-y", "mcp-remote@latest", "https://api.mcpanalytics.ai/auth0"]
    }
  }
}

✨ Features

Natural Language Interface

You can describe your needs in natural language, such as:

"What drives our revenue growth?"
"Find customer segments in our data"
"Forecast next quarter's sales"
"Did our marketing campaign work?"

Comprehensive Analysis Suite

The platform offers a wide range of analysis methods:

Seamless Workflow

The workflow can be visualized as follows:

graph LR
    A[Ask in Claude/Cursor] --> B[MCP Analytics]
    B --> C[Secure Processing]
    C --> D[Interactive Report]
    D --> E[Share Results]

📦 Installation

Installation steps have been provided in the "Quick Start" section.

💻 Usage Examples

Basic Regression

User: "I have a CSV with house prices. Can you predict price based on size and location?"
Claude: [Runs linear regression, provides R², coefficients, and diagnostic plots]

Customer Segmentation

User: "Segment my customers in sales_data.csv into meaningful groups"
Claude: [Performs k-means clustering, creates segment profiles with visualizations]

Time Series Forecasting

User: "Forecast next quarter's revenue using our historical data"
Claude: [Applies ARIMA, generates predictions with confidence intervals]

📚 Documentation

• Quick Start Guide - Get running in 30 seconds
• API Reference - Complete API documentation
• Platform Overview - How the platform works
• Tutorials - Step-by-step guides
• Examples - Real-world use cases
• Security - Security & compliance details

🔧 Technical Details

How It Works

The MCP Analytics Workflow

1. Ask Your Question - Describe what you want to analyze in natural language
2. Intelligent Discovery - tools.discover finds the right analytical approach
3. Data Upload - datasets.upload securely processes your data
4. Automated Analysis - tools.run executes with optimal configuration
5. Interactive Results - reports.view delivers shareable insights

User: "What drives our sales growth?"
MCP Analytics:
  → Discovers regression and correlation methods
  → Configures analysis for your data structure
  → Runs multiple analytical approaches
  → Returns comprehensive report with insights

MCP Tools

The platform provides a complete suite of MCP tools for end-to-end analytics:

Core Analytics Tools

• tools.discover - Natural language tool discovery
• tools.run - Automated analysis execution
• tools.info - Get tool documentation

Data Management

• datasets.upload - Secure data upload with encryption
• datasets.list - Manage your datasets
• datasets.read - Access and preview data

Reporting & Insights

• reports.view - Interactive visualization dashboard
• reports.search - Semantic search across analyses

Platform Tools

• billing() - Usage and subscription management
• about() - Platform information and status
• manual() - Documentation access

Architecture

flowchart TB
    subgraph "Client Integration"
        CLI[CLI/SDK]
        Claude[Claude Desktop]
        Cursor[Cursor IDE]
        MCP[MCP Protocol]
    end

    subgraph "API Gateway"
        LB[Load Balancer]
        Auth[OAuth 2.0/Auth0]
        Rate[Rate Limiting]
    end

    subgraph "Processing Layer"
        Router[Request Router]
        Queue[Job Queue]
        Workers[Processing Workers]
        Docker[Docker Containers]
    end

    subgraph "Analytics Engine"
        Stats[Statistical Methods]
        ML[Machine Learning]
        TS[Time Series]
        Report[Report Generation]
    end

    subgraph "Data Layer"
        Cache[Results Cache]
        Storage[Secure Storage]
        Encrypt[Encryption Layer]
    end

    CLI --> LB
    Claude --> LB
    Cursor --> LB
    MCP --> LB

    LB --> Auth
    Auth --> Rate
    Rate --> Router

    Router --> Queue
    Queue --> Workers
    Workers --> Docker

    Docker --> Stats
    Docker --> ML
    Docker --> TS

    Stats --> Report
    ML --> Report
    TS --> Report

    Report --> Cache
    Cache --> Storage
    Storage --> Encrypt

    style Auth fill:#e8f5e9
    style Docker fill:#fff3e0
    style Report fill:#e3f2fd

Performance

• Dataset Size: Handles large datasets
• Processing Time: Fast cloud-based processing
• Secure Infrastructure: Isolated Docker containers
• API Access: RESTful API with authentication

📄 License

Copyright © 2025 PeopleDrivenAI LLC. All Rights Reserved.

MCP Analytics is a product of PeopleDrivenAI LLC.

This is commercial software. Use of the MCP Analytics service is subject to our:

• Terms of Service
• Privacy Policy
• Acceptable Use Policy