# embeddedlayers/mcp-analytics [Health: Active] **Category:** 👤 Customer Data Platforms **Repository:** https://github.com/embeddedlayers/mcp-analytics **GitHub Stars:** 7 **Views:** 2 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/embeddedlayers-mcp-analytics ## Description Statistical analysis, forecasting, and ML for business data (Shopify, Stripe, WooCommerce, eBay, GA4, Search Console). Upload a CSV or connect live data sources — ask a question in Claude or Cursor, get an interactive HTML report. ## Tools Capabilities this server exposes over MCP: - **create_analysis** - **build_status** - **run_analysis** - **discover_tools** - **modify_analysis** - **tools_schema** - **datasets_upload** - **datasets_list** - **connectors_list** - **connectors_query** - **reports_view** - **reports_list** - **report_cards** - **ask_library** - **agent_advisor** - **billing** - **account_link** - **about** ## Claude Desktop Quick Installation Install path detected from listing signals. Uses `npx` (confidence: high): ```json "mcpServers": { "mcp-analytics": { "command": "npx", "args": ["-y","Install"], "env": { "MCP_ANALYTICS_API_KEY": "" } } } ``` **Requires environment variables:** `MCP_ANALYTICS_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for). ## Documentation & README # MCP Analytics Suite **The statistical analyst in your AI chat.** Bring a CSV (or connect a live source) and a question. A standing team of specialist agents builds a custom analysis specific to your data, validates the methodology, and ships back a citable, interactive report. The analysis 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 →](https://mcpanalytics.ai/case-studies) • [Try Demo →](https://mcpanalytics.ai/demo) • [Pricing →](https://mcpanalytics.ai/pricing) **Try it before installing anything.** The [free tools](https://mcpanalytics.ai/free/) run in the browser on a CSV you upload — no account, no key, no MCP client. Each one is a real analysis with the method written out: [PCA](https://mcpanalytics.ai/free/standard_pca), [correlation](https://mcpanalytics.ai/free/standard_correlation), [forecasting](https://mcpanalytics.ai/free/standard_forecasting), [RFM segmentation](https://mcpanalytics.ai/free/standard_rfm), [regression (GLM)](https://mcpanalytics.ai/free/standard_glm).
[![Glama Score](https://glama.ai/mcp/servers/embeddedlayers/mcp-analytics/badges/score.svg)](https://glama.ai/mcp/servers/embeddedlayers/mcp-analytics) [![npm](https://img.shields.io/npm/v/@mcp-analytics/mcp-analytics)](https://www.npmjs.com/package/@mcp-analytics/mcp-analytics) [![License](https://img.shields.io/badge/License-MIT-green)](LICENSE) [![Platform](https://img.shields.io/badge/Platform-MCP_Compatible-blue)](https://mcpanalytics.ai/install) [![Docs](https://img.shields.io/badge/Docs-mcpanalytics.ai-brightgreen)](https://mcpanalytics.ai/docs) **Hire the team. Own the analysis. Rerun forever.** [🚀 Quick Start](#quick-start) • [🔄 How It Works](#how-it-works) • [🛠️ MCP Tools](#mcp-tools) • [🛡️ Security](#security--compliance) • [📖 Documentation](#documentation)
[![Demo Video](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/assets/demo-preview.png)](https://github.com/embeddedlayers/mcp-analytics/releases/download/v1.0.4/demo.mp4) *Click to watch: Ask a question → upload data → get an interactive report with AI insights*
--- ## 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 for your data. The result is an interactive report: charts, AI-narrated insights, exportable PDF, embedded source code, citable. Every commissioned analysis 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 analysis creation** is the named revenue event — pay once to build the capability, own it, rerun for a fraction of the creation price. A build that fails is never billed. Connect data however it lives: CSV upload, public URL, or live OAuth connectors for Google Analytics 4 and Google Search Console (more coming). Once a connector is linked, every rerun pulls fresh data automatically — no re-export step. ### Choose Your Depth — Four Tiers Every analysis runs through the same pipeline — you choose how far it goes: | Tier | What you get | Time | |------|-------------|------| | **Snapshot** | One chart and a verified insight — an instant read of your data, covered by your welcome credits | ~2 min | | **JSON** | One computed statistical answer — the numbers and the method — deployed as a tool you re-run on fresh data | ~5 min | | **Brief** | The computed answer, presented — chart, key figures, and method on a single shareable page | ~7 min | | **Deck** | The full study — a complete statistical report built to your brief and independently verified; a durable module you own and re-run forever | 30–45 min | More rigor outranks more charts: going deeper buys real statistical methods — hypothesis tests, regression, diagnostics — not just more cards. You pay for depth, and only if the build succeeds. [How the tiers work →](https://mcpanalytics.ai/tiers) ### 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, named 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 [account.mcpanalytics.ai](https://account.mcpanalytics.ai), go to account settings, and copy your API key (starts with `mcp_`). You get **9,000 welcome credits**, no credit card required. That covers about seven full analyses at any depth, plus re-runs. ### 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 1. **Upload your data** — `datasets_upload` securely processes your CSV (or reuse an existing dataset / connected source) 2. **Commission the analysis** — `create_analysis` takes your question in plain language, your dataset, and the tier you choose (snapshot, json, brief, or deck) 3. **Watch it build** — `build_status` reports progress, queue position, and the report link when done 4. **Get the report** — `reports_view` delivers the interactive report; `report_cards` displays individual cards inline 5. **Rerun forever** — `run_analysis` re-runs any analysis you own on fresh data for a fraction of the creation cost ``` User: "What drives our sales growth?" MCP Analytics: → Scopes the right statistical method for your data's shape → Writes R in an isolated container — deterministic, fixed seeds → Runs it, then independently verifies numbers and narrative → Returns a citable, interactive report you own ``` ## MCP Tools The platform provides a complete suite of MCP tools for end-to-end analytics: ### Analysis - **`create_analysis`** - Commission a new analysis from a plain-language question, at the tier you choose - **`build_status`** - Track a build: stage progress, queue position, report link - **`run_analysis`** - Run an analysis you own (or one discovered via `discover_tools`) on fresh data - **`modify_analysis`** - Turn an existing analysis into a new version — reword the question, change the framing ### Discovery - **`discover_tools`** - Browse what you can run: your commissioned analyses plus the prebuilt library - **`tools_schema`** - Get an analysis's parameter schema — always call this before `run_analysis` ### Data Management - **`datasets_upload`** - Secure data upload with encryption - **`datasets_list`** - List and search your uploaded datasets ### Connectors - **`connectors_list`** - List available data source connections - **`connectors_query`** - Pull live data from a connected source ### Reporting & Insights - **`reports_view`** - Get a shareable browser link for a report - **`reports_list`** - Your report library — every analysis delivered, searchable in plain language - **`report_cards`** - Browse a delivered report's individual cards (charts, tables, insights) - **`ask_library`** - Ask one question across *all* your delivered analyses; get a synthesized answer with citations back to each source report - **`agent_advisor`** - AI help desk — which analysis fits your question, and how to read the result ### Platform Tools - **`billing`** - Usage and credit management - **`account_link`** - Link to the right account page for anything not doable in chat - **`about`** - Platform documentation and info — how it works, tiers, usage > Browse the catalog yourself, without an account: > `curl -X POST https://api.mcpanalytics.ai/mcp/discover -H 'Content-Type: application/json' -d '{"jsonrpc":"2.0","method":"tools/list","id":1,"params":{}}'` > Discovery returns the 15 tools that work pre-auth; `billing`, `connectors_list`, > and `connectors_query` appear once you connect with a key or via OAuth. ## 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 ```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 ``` 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 →**](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/SECURITY.md) ## 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 →**](https://mcpanalytics.ai) ## Documentation - [**Quick Start Guide**](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/docs/quickstart.md) - Get running in under a minute - [**Architecture**](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/docs/ARCHITECTURE.md) - How the platform works - [**Connectors**](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/docs/connectors.md) - GA4, GSC, and CSV data sources - [**Pricing**](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/docs/pricing.md) - Credits, tiers, and plans - [**How Credits Work**](https://mcpanalytics.ai/how-credits-work) - The credit model explained - [**Security**](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/SECURITY.md) - Security & compliance details - [**Tutorials**](https://mcpanalytics.ai/tutorials) - Step-by-step guides ## Support - **Issues**: [GitHub Issues](https://github.com/embeddedlayers/mcp-analytics/issues) - **Email**: support@mcpanalytics.ai - **Docs**: [mcpanalytics.ai/docs](https://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 →**](https://mcpanalytics.ai/compare) ## 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 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 `create_analysis`, `discover_tools`, `datasets_upload`, etc. ```bash # Test the stdio proxy directly: MCP_ANALYTICS_API_KEY=mcp_your_key npx -y @mcp-analytics/mcp-analytics # Should output a "[mcp-analytics] Connected to https://api.mcpanalytics.ai" line with the tool count ``` ### 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](https://github.com/embeddedlayers/mcp-analytics/blob/HEAD/CONTRIBUTING.md) for guidelines. ## License Copyright © 2026 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](https://mcpanalytics.ai/terms) - [Privacy Policy](https://mcpanalytics.ai/privacy) ---
**Ready to transform your data analysis workflow?** [**Get Started Free**](https://mcpanalytics.ai/signup) | [**Read Docs**](https://mcpanalytics.ai/docs) | [**View Demo**](https://mcpanalytics.ai/demo) Built by [MCP Analytics](https://mcpanalytics.ai) | Powered by R & Python
--- 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`