Analytics MCP

MCP Analytics Suite

⚠️ Beta — v2 rebuild in progress. We're actively rebuilding the platform. Some features are incomplete or unstable right now. You can sign up and test at mcpanalytics.ai, or subscribe to the launch newsletter. Details: #22 — v2 rebuild: what's changing, what to expect.

Adhoc analysis generation, on your data, on demand. Bring a CSV (or connect a live source — Shopify, Stripe, GA4, GSC, and more) and a question. A standing team of specialist agents builds a custom analysis module for your specific data, validates the methodology, and ships back a citable, interactive report. The module is yours — it lives in your library, reruns on fresh data for a fraction of the creation cost, and is queryable from Claude, Cursor, or any MCP client. The work compounds.

This is the public listing and documentation repository. Issues, feature requests, and examples live here. The API server code is maintained separately.

Sample Reports → • Try Demo → • Pricing →

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Overview

You bring data and a question. A pipeline of specialist agents — spec drafter, builder, verifier, fixer, deployer — turns your question into a custom analysis module for your data. The module produces an interactive report: charts, AI-narrated insights, exportable PDF, embedded source code, citable. After creation, the module joins your private library — query it from any MCP client, rerun on fresh data with one call, share with collaborators on your terms.

Cornerstone modules ship pre-built (t-tests, regression, churn, segmentation, forecasting, customer LTV, A/B testing, time series, survival analysis, and more) so you can see a finished report in under a minute and verify the team can build things that work. Custom module creation is the named revenue event — pay once to build the capability, own it, rerun for a fraction of the creation price.

Connect data however it lives: CSV upload, public URL, or live OAuth connectors for Shopify, Stripe, Google Analytics 4, and Google Search Console (more coming). Once a connector is linked, every rerun pulls fresh data automatically — no re-export step.

Why MCP Analytics

• Citable — APA / MLA / Chicago / BibTeX in one click, ready for papers, decks, and regulatory filings
• Sourceable — R source code embedded in every report; a skeptical reader can run it and get the same answer
• Reproducible — fixed seeds, Docker isolation, validated methods; same input → same output, forever
• Yours — every commissioned module is private to your account; rerun on fresh data, query across your portfolio
• MCP-native — query the library from Claude, Cursor, Windsurf, or any MCP client
• Secure — OAuth2, encryption at rest, isolated container processing per analysis
• Honest — when an analysis has issues, the team gives you a free re-run; the relationship is built on the report being right

Quick Start

1. Get an API Key

Sign up free at app.mcpanalytics.ai, go to account settings, and copy your API key (starts with mcp_). You get 2,000 free credits — no credit card required.

2. Connect

Three options — all connect to the same platform with the same tools.

Option A: npx Install (Recommended)

Works with Claude Desktop, Cursor, Windsurf, and any stdio MCP client. Requires Node.js 18+.

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "mcpanalytics": {
      "command": "npx",
      "args": ["-y", "@mcp-analytics/mcp-analytics"],
      "env": {
        "MCP_ANALYTICS_API_KEY": "mcp_your_key_here"
      }
    }
  }
}

Cursor / Windsurf — add to .cursor/mcp.json:

{
  "mcpServers": {
    "mcpanalytics": {
      "command": "npx",
      "args": ["-y", "@mcp-analytics/mcp-analytics"],
      "env": {
        "MCP_ANALYTICS_API_KEY": "mcp_your_key_here"
      }
    }
  }
}

Claude Code — run in your terminal:

claude mcp add mcpanalytics -- npx -y @mcp-analytics/mcp-analytics
# Then set MCP_ANALYTICS_API_KEY in your environment

Option B: Direct API Key (No npm)

For MCP clients that support Streamable HTTP transport with custom headers:

{
  "mcpServers": {
    "mcpanalytics": {
      "url": "https://api.mcpanalytics.ai/mcp/api-key",
      "headers": {
        "X-API-Key": "mcp_your_key_here"
      }
    }
  }
}

Option C: OAuth2 (No API Key)

Zero-config — a browser opens for login on first connection:

{
  "mcpServers": {
    "mcpanalytics": {
      "url": "https://api.mcpanalytics.ai/auth0"
    }
  }
}

Browse Tools First (No Account Needed)

Explore the full tool catalog before signing up:

# Static metadata (tool names, descriptions, all transport options)
curl https://api.mcpanalytics.ai/.well-known/mcp.json

# MCP protocol discovery (no auth — works with any MCP client)
curl -X POST https://api.mcpanalytics.ai/mcp/discover \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":1,"params":{}}'

3. Start Analyzing

Restart your MCP client. Ask:

• "Upload sales.csv and find what drives revenue"
• "What statistical test should I use for this survey data?"
• "Forecast next quarter's sales from this time series"

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

• discover_tools - Natural language tool discovery (5-signal semantic search)
• tools_run - Execute an analysis module on your data
• tools_info - Get tool documentation and schema
• tools_schema - Inspect column requirements for a tool

Data Management

• datasets_upload - Secure data upload with encryption
• datasets_list - List your uploaded datasets
• datasets_read - Preview dataset contents
• datasets_download - Download a dataset
• datasets_update - Update dataset metadata

Connectors

• connectors_list - List available data source connections
• connectors_query - Pull live data from a connected source

Reporting & Insights

• reports_view - Open an interactive HTML report
• reports_list - List your reports
• reports_search - Semantic search across past analyses
• agent_advisor - Conversational AI that guides analysis and interprets results

Platform Tools

• billing - Usage and subscription management
• about - Platform information and status

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: OAuth 2.0 scoped permissions 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 under a minute
• Architecture - How the platform works
• Connectors - GA4, GSC, and CSV data sources
• Pricing - Plans and limits
• Security - Security & compliance details
• API Reference - Complete API documentation
• Tutorials - Step-by-step guides

Support

• Issues: GitHub Issues
• 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 data scientists and engineers passionate about making advanced statistical analysis accessible through AI assistants. The platform runs validated, deterministic analysis modules — the same data and tool produce the same result every time, unlike LLM code generation.

Testing & Support

Testing Your Connection

After installation, restart your MCP client and look for "MCP Analytics" in the available tools. You should see tools like discover_tools, tools_run, datasets_upload, etc.

# Test the stdio proxy directly:
MCP_ANALYTICS_API_KEY=mcp_your_key npx -y @mcp-analytics/mcp-analytics
# Should output: "[mcp-analytics] Connected to https://api.mcpanalytics.ai. 19 tools available."

Troubleshooting

If MCP Analytics doesn't appear after installation:

1. Ensure your config file is valid JSON
2. Restart your MCP client completely
3. Verify your API key starts with mcp_
4. Check the client's developer console for errors
5. Try running the npx command in a terminal to see errors

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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If MCP Analytics saves you time, a ⭐ on GitHub helps others find it.

Tags: mcp mcp-server model-context-protocol analytics data-analytics shopify-analytics stripe-analytics csv-analysis statistics machine-learning time-series clustering regression business-intelligence claude cursor ai-tools no-code-analytics forecasting customer-analytics