ai.mcpanalytics/analytics

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The statistical analyst in your AI chat — validated, citable, re-runnable analysis of your data.

Statistical analysis tool for AI assistants to perform validated, reproducible data analysis in conversations.

statisticsdata analysishypothesis testingreproducible researchanalytics

Topics

analyticsclaudecursordata-sciencemachine-learningmcpmodel-context-protocolstatisticsdata-analyticsmcp-serverai-agentsai-toolsanthropicbusiness-intelligencecsv-analysisinteractive-reportsllm-toolsno-codeshopify-analyticsstripe-analytics

README.md16 KBView on GitHub

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 →

Hire the team. Own the analysis. Rerun forever.

🚀 Quick Start • 🔄 How It Works • 🛠️ MCP Tools • 🛡️ Security • 📖 Documentation

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Click to watch: Ask a question → upload data → get an interactive report with AI insights

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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):

json

{
  "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:

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:

bash

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:

json

{
  "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:

json

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

Browse Tools First (No Account Needed)

Explore the full tool catalog before signing up:

bash

# 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

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

code

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:

code

"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

</td> <td width="50%">

Machine Learning

Ensemble Methods
Boosting Algorithms
Neural Networks
Clustering
Dimensionality Reduction

</td> </tr> <tr> <td width="50%">

Time Series

Forecasting
Seasonal Analysis
Trend Detection
Multivariate Models
Causal Analysis

</td> <td width="50%">

Business Analytics

Customer Analytics
Market Analysis
Pricing Models
Predictive Analytics
Experimental Design

</td> </tr> </table>

Seamless Workflow

mermaid

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

code

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

code

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

Time Series Forecasting

code

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

mermaid

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.

bash

# 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:

Ensure your config file is valid JSON
Restart your MCP client completely
Verify your API key starts with mcp_
Check the client's developer console for errors
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

MCP Analytics is a product of PeopleDrivenAI LLC.

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

Ready to transform your data analysis workflow?

Get Started Free | Read Docs | View Demo

Built by MCP Analytics | Powered by R & Python

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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

Install

claude_desktop_config.json

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

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.