MCP Analytics Suite

Every analysis starts with a question. We handle the rest.

[🚀 Quick Start](# quick-start) • [🔄 How It Works](# how-it-works) • [🛠️ MCP Tools](# mcp-tools) • [🛡️ Security](# security- -compliance) • [📖 Documentation](# documentation)

# The Formula

# Overview

MCP Analytics Suite is an intelligent analytics platform that understands what you want to analyze and automatically selects the right approach. No statistics degree required—just describe your business question and let our AI-powered discovery handle the complexity.

# # Why MCP Analytics?

• Intelligent Discovery: Automatically finds the right analytical approach
• Complete Workflow: From question to insight in one seamless flow
• Zero Setup: Cloud-based processing, works instantly
• Enterprise Security: OAuth2, encryption, isolated processing
• Comprehensive Suite: Full range of analytical capabilities
• Interactive Reports: Shareable visualizations with AI insights

# Quick Start

# # Installation

# # # # For Claude Desktop

Add 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 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 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 to claude_code_config.json:

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

# 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

# Features

# # Natural Language Interface

Just describe what you need:

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

# # Comprehensive Analysis Suite

Statistical Methods

• Regression Analysis
• Advanced Modeling
• Hypothesis Testing
• Survival Analysis
• Bayesian Methods

Machine Learning

• Ensemble Methods
• Boosting Algorithms
• Neural Networks
• Clustering
• Dimensionality Reduction

Time Series

• Forecasting
• Seasonal Analysis
• Trend Detection
• Multivariate Models
• Causal Analysis

Business Analytics

• Customer Analytics
• Market Analysis
• Pricing Models
• Predictive Analytics
• Experimental Design

# # Seamless Workflow

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

# Example Usage

# # 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]

# Security & Compliance

# # Enterprise Security Features

• Authentication: OAuth2 via Auth0 with PKCE
• Encryption: TLS 1.3 for all data transfers
• Processing: Isolated Docker containers per analysis
• Data Handling: Ephemeral processing, no persistence
• Access Control: API key management with usage limits
• Audit Trail: Complete logging for compliance

# # Privacy & Data Handling

• Data Privacy: Ephemeral processing, no data retention
• User Rights: Data deletion upon request
• Secure Processing: Isolated containers per analysis
• Enterprise Options: Contact us for compliance requirements

Read full security documentation →

# 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

# Getting Started

Visit our website for pricing and signup →

# 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

# Support

• Issues: GitHub Issues
• Discord: Join our community
• Email: support@mcpanalytics.ai
• Docs: mcpanalytics.ai/docs
• Enterprise: sales@mcpanalytics.ai

# Comparison with Other MCP Servers

| Feature | MCP Analytics | Google Analytics MCP | PostgreSQL MCP | Filesystem MCP |
|- -- -- -- --|- -- -- -- -- -- -- -|- -- -- -- -- -- -- -- -- -- --|- -- -- -- -- -- -- -- -|- -- -- -- -- -- -- -- -|
| Use Case | Statistical Analysis | Web Metrics | Database Queries | File Access |
| Setup Time | 30 seconds | OAuth + Config | Connection string | Path config |
| Data Sources | Any CSV/JSON/URL | GA4 Only | PostgreSQL Only | Local files |
| Analysis Tools | Full Suite | GA4 Metrics | SQL Only | Read/Write |
| Machine Learning | ✅ Full Suite | ❌ | ❌ | ❌ |
| Visualizations | ✅ Interactive | ✅ Dashboards | ❌ | ❌ |
| Shareable Reports | ✅ | ❌ | ❌ | ❌ |

Detailed comparison →

# About MCP Analytics

MCP Analytics is built by a team of data scientists and engineers passionate about making advanced analytics accessible through AI. We're backed by enterprise customers across finance, healthcare, and e-commerce.

# # Coming Soon

• NPM Package: Direct installation via npm install @mcpanalytics/server
• Smithery Integration: One-click install via Smithery CLI
• MCP Registry: Official listing in the MCP servers directory
• More Tools: Continuously expanding our analytics capabilities

# Testing & Support

# # Testing Your Connection

After installation, restart your IDE and look for "MCP Analytics" in the available tools. On first use, you'll be prompted to authenticate via OAuth 2.0.

#  To test the connection directly:
npx -y mcp-remote@latest https://api.mcpanalytics.ai/auth0

# # Troubleshooting

If MCP Analytics doesn't appear after installation:

1. Ensure your config file is valid JSON
2. Restart your IDE completely
3. Check the IDE's developer console for errors
4. Verify you have internet connectivity

For support: support@mcpanalytics.ai

# Contributing

While the core server is proprietary, we welcome contributions to:

• Documentation improvements
• Example notebooks and use cases
• Bug reports and feature requests
• Community tools and integrations

See CONTRIBUTING.md for guidelines.

# 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

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