[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_125":126},"routes/modules.$creator.$moduleId",{"_14":15,"_16":17,"_18":19,"_20":21},"routes/modules.$creator.$moduleId.endpoints",{"_11":12,"_13":-5},"error","Module '@embeddedlayers/mcp-analytics' is not configured","tools","module",{"_20":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":26,"_34":-5,"_35":36,"_37":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":48,"_56":57,"_58":59,"_60":-5,"_61":-5,"_62":-5,"_63":-5,"_64":65,"_66":67,"_68":69,"_70":71,"_72":73,"_74":-5,"_75":-5,"_76":-5,"_77":48,"_78":-5,"_79":80,"_81":82,"_83":84,"_85":86,"_87":88,"_89":90,"_91":92},"apiUrl","https://odel.app/api","creator","embeddedlayers","moduleId","mcp-analytics","@embeddedlayers/mcp-analytics","displayName","mcp analytics","tagline","Analytics for business data: upload CSV or connect GA4/GSC, run ML/stats, get HTML reports.","iconKey","https://avatars.githubusercontent.com/u/153229593?v=4","category","data","status","published","metaDescription","metaKeywords","deploymentType","external","workerName","bindingName","mcpConfig","{\"url\":\"https://api.mcpanalytics.ai/auth0\",\"transport\":\"streamable-http\",\"authType\":\"none\"}","pricingModel","free","creatorUserId","system:external-import","createdAt",1781363197000,"updatedAt",1781499297000,"sourceRegistry","official-mcp-registry","sourceUrl","https://registry.modelcontextprotocol.io/v0/servers?search=ai.mcpanalytics%2Fanalytics","sourceVerified",false,"importedAt","upstreamStars",6,"upstreamUpdatedAt",1778362980000,"detectedTransport","detectedAuthType","probeStatus","lastProbedAt","githubRepoUrl","https://github.com/embeddedlayers/mcp-analytics","githubRepoFullName","embeddedlayers/mcp-analytics","releaseId","ext-embeddedlayers-mcp-analytics","version","1.0.5","descriptionMarkdown","# MCP Analytics Suite\n\n> ⚠️ **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](https://mcpanalytics.ai), or subscribe to the [launch newsletter](https://mcpanalytics.ai/#newsletter-footer). Details: [#22 — v2 rebuild: what's changing, what to expect](https://github.com/embeddedlayers/mcp-analytics/issues/22).\n\n**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.\n\n> **This is the public listing and documentation repository.** Issues, feature requests, and examples live here. The API server code is maintained separately.\n\n[Sample Reports →](https://mcpanalytics.ai/sample-reports.html) • [Try Demo →](https://mcpanalytics.ai/demo.html) • [Pricing →](https://mcpanalytics.ai/pricing.html)\n\n

\n\n[![Glama Score](https://glama.ai/mcp/servers/embeddedlayers/mcp-analytics/badges/score.svg)](https://glama.ai/mcp/servers/embeddedlayers/mcp-analytics)\n[![npm](https://img.shields.io/npm/v/@mcp-analytics/mcp-analytics)](https://www.npmjs.com/package/@mcp-analytics/mcp-analytics)\n[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)\n[![Platform](https://img.shields.io/badge/Platform-MCP_Compatible-blue)](https://mcpanalytics.ai/install)\n[![Docs](https://img.shields.io/badge/Docs-mcpanalytics.ai-brightgreen)](https://mcpanalytics.ai/docs)\n\n**Hire the team. Own the analysis. Rerun forever.**\n\n[🚀 Quick Start](#quick-start) • [🔄 How It Works](#how-it-works) • [🛠️ MCP Tools](#mcp-tools) • [🛡️ Security](#security--compliance) • [📖 Documentation](#documentation)\n\n

\n\n

\n\n[![Demo Video](https://raw.githubusercontent.com/embeddedlayers/mcp-analytics/main/assets/demo-preview.png)](https://github.com/embeddedlayers/mcp-analytics/releases/download/v1.0.4/demo.mp4)\n\n*Click to watch: Ask a question → upload data → get an interactive report with AI insights*\n\n

\n\n---\n\n## Overview\n\nYou 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.\n\n**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.\n\nConnect 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.\n\n### Why MCP Analytics\n\n- **Citable** — APA / MLA / Chicago / BibTeX in one click, ready for papers, decks, and regulatory filings\n- **Sourceable** — R source code embedded in every report; a skeptical reader can run it and get the same answer\n- **Reproducible** — fixed seeds, Docker isolation, validated methods; same input → same output, forever\n- **Yours** — every commissioned module is private to your account; rerun on fresh data, query across your portfolio\n- **MCP-native** — query the library from Claude, Cursor, Windsurf, or any MCP client\n- **Secure** — OAuth2, encryption at rest, isolated container processing per analysis\n- **Honest** — when an analysis has issues, the team gives you a free re-run; the relationship is built on the report being right\n\n## Quick Start\n\n### 1. Get an API Key\n\nSign up free at [app.mcpanalytics.ai](https://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.\n\n### 2. Connect\n\nThree options — all connect to the same platform with the same tools.\n\n#### Option A: npx Install (Recommended)\n\nWorks with Claude Desktop, Cursor, Windsurf, and any stdio MCP client. Requires Node.js 18+.\n\n**Claude Desktop** — add to `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\\Claude\\claude_desktop_config.json` (Windows):\n\n```json\n{\n \"mcpServers\": {\n \"mcpanalytics\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@mcp-analytics/mcp-analytics\"],\n \"env\": {\n \"MCP_ANALYTICS_API_KEY\": \"mcp_your_key_here\"\n }\n }\n }\n}\n```\n\n**Cursor / Windsurf** — add to `.cursor/mcp.json`:\n\n```json\n{\n \"mcpServers\": {\n \"mcpanalytics\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@mcp-analytics/mcp-analytics\"],\n \"env\": {\n \"MCP_ANALYTICS_API_KEY\": \"mcp_your_key_here\"\n }\n }\n }\n}\n```\n\n**Claude Code** — run in your terminal:\n\n```bash\nclaude mcp add mcpanalytics -- npx -y @mcp-analytics/mcp-analytics\n# Then set MCP_ANALYTICS_API_KEY in your environment\n```\n\n#### Option B: Direct API Key (No npm)\n\nFor MCP clients that support Streamable HTTP transport with custom headers:\n\n```json\n{\n \"mcpServers\": {\n \"mcpanalytics\": {\n \"url\": \"https://api.mcpanalytics.ai/mcp/api-key\",\n \"headers\": {\n \"X-API-Key\": \"mcp_your_key_here\"\n }\n }\n }\n}\n```\n\n#### Option C: OAuth2 (No API Key)\n\nZero-config — a browser opens for login on first connection:\n\n```json\n{\n \"mcpServers\": {\n \"mcpanalytics\": {\n \"url\": \"https://api.mcpanalytics.ai/auth0\"\n }\n }\n}\n```\n\n#### Browse Tools First (No Account Needed)\n\nExplore the full tool catalog before signing up:\n\n```bash\n# Static metadata (tool names, descriptions, all transport options)\ncurl https://api.mcpanalytics.ai/.well-known/mcp.json\n\n# MCP protocol discovery (no auth — works with any MCP client)\ncurl -X POST https://api.mcpanalytics.ai/mcp/discover \\\n -H 'Content-Type: application/json' \\\n -d '{\"jsonrpc\":\"2.0\",\"method\":\"tools/list\",\"id\":1,\"params\":{}}'\n```\n\n### 3. Start Analyzing\n\nRestart your MCP client. Ask:\n\n- *\"Upload sales.csv and find what drives revenue\"*\n- *\"What statistical test should I use for this survey data?\"*\n- *\"Forecast next quarter's sales from this time series\"*\n\n## How It Works\n\n### The MCP Analytics Workflow\n\n1. **Ask Your Question** - Describe what you want to analyze in natural language\n2. **Intelligent Discovery** - `tools.discover` finds the right analytical approach\n3. **Data Upload** - `datasets.upload` securely processes your data\n4. **Automated Analysis** - `tools.run` executes with optimal configuration\n5. **Interactive Results** - `reports.view` delivers shareable insights\n\n```\nUser: \"What drives our sales growth?\"\nMCP Analytics:\n → Discovers regression and correlation methods\n → Configures analysis for your data structure\n → Runs multiple analytical approaches\n → Returns comprehensive report with insights\n```\n\n## MCP Tools\n\nThe platform provides a complete suite of MCP tools for end-to-end analytics:\n\n### Core Analytics Tools\n- **`discover_tools`** - Natural language tool discovery (5-signal semantic search)\n- **`tools_run`** - Execute an analysis module on your data\n- **`tools_info`** - Get tool documentation and schema\n- **`tools_schema`** - Inspect column requirements for a tool\n\n### Data Management\n- **`datasets_upload`** - Secure data upload with encryption\n- **`datasets_list`** - List your uploaded datasets\n- **`datasets_read`** - Preview dataset contents\n- **`datasets_download`** - Download a dataset\n- **`datasets_update`** - Update dataset metadata\n\n### Connectors\n- **`connectors_list`** - List available data source connections\n- **`connectors_query`** - Pull live data from a connected source\n\n### Reporting & Insights\n- **`reports_view`** - Open an interactive HTML report\n- **`reports_list`** - List your reports\n- **`reports_search`** - Semantic search across past analyses\n- **`agent_advisor`** - Conversational AI that guides analysis and interprets results\n\n### Platform Tools\n- **`billing`** - Usage and subscription management\n- **`about`** - Platform information and status\n\n## Features\n\n### Natural Language Interface\n\nJust describe what you need:\n\n```\n\"What drives our revenue growth?\"\n\"Find customer segments in our data\"\n\"Forecast next quarter's sales\"\n\"Did our marketing campaign work?\"\n```\n\n### Comprehensive Analysis Suite\n\n\n\n\n\n\n\n\n\n\n

\n\nStatistical Methods\n- Regression Analysis\n- Advanced Modeling\n- Hypothesis Testing\n- Survival Analysis\n- Bayesian Methods\n\n	\n\nMachine Learning\n- Ensemble Methods\n- Boosting Algorithms\n- Neural Networks\n- Clustering\n- Dimensionality Reduction\n\n
\n\nTime Series\n- Forecasting\n- Seasonal Analysis\n- Trend Detection\n- Multivariate Models\n- Causal Analysis\n\n	\n\nBusiness Analytics\n- Customer Analytics\n- Market Analysis\n- Pricing Models\n- Predictive Analytics\n- Experimental Design\n\n

\n\n### Seamless Workflow\n\n```mermaid\ngraph LR\n A[Ask in Claude/Cursor] --> B[MCP Analytics]\n B --> C[Secure Processing]\n C --> D[Interactive Report]\n D --> E[Share Results]\n```\n\n\n## Example Usage\n\n### Basic Regression\n```\nUser: \"I have a CSV with house prices. Can you predict price based on size and location?\"\nClaude: [Runs linear regression, provides R², coefficients, and diagnostic plots]\n```\n\n### Customer Segmentation\n```\nUser: \"Segment my customers in sales_data.csv into meaningful groups\"\nClaude: [Performs k-means clustering, creates segment profiles with visualizations]\n```\n\n### Time Series Forecasting\n```\nUser: \"Forecast next quarter's revenue using our historical data\"\nClaude: [Applies ARIMA, generates predictions with confidence intervals]\n```\n\n## Security & Compliance\n\n### Enterprise Security Features\n\n- **Authentication**: OAuth2 via Auth0 with PKCE\n- **Encryption**: TLS 1.3 for all data transfers\n- **Processing**: Isolated Docker containers per analysis\n- **Data Handling**: Ephemeral processing, no persistence\n- **Access Control**: OAuth 2.0 scoped permissions with usage limits\n- **Audit Trail**: Complete logging for compliance\n\n### Privacy & Data Handling\n\n- **Data Privacy**: Ephemeral processing, no data retention\n- **User Rights**: Data deletion upon request\n- **Secure Processing**: Isolated containers per analysis\n- **Enterprise Options**: Contact us for compliance requirements\n\n[**Read full security documentation →**](https://mcpanalytics.ai/security)\n\n## Architecture\n\n```mermaid\nflowchart TB\n subgraph \"Client Integration\"\n CLI[CLI/SDK]\n Claude[Claude Desktop]\n Cursor[Cursor IDE]\n MCP[MCP Protocol]\n end\n\n subgraph \"API Gateway\"\n LB[Load Balancer]\n Auth[OAuth 2.0/Auth0]\n Rate[Rate Limiting]\n end\n\n subgraph \"Processing Layer\"\n Router[Request Router]\n Queue[Job Queue]\n Workers[Processing Workers]\n Docker[Docker Containers]\n end\n\n subgraph \"Analytics Engine\"\n Stats[Statistical Methods]\n ML[Machine Learning]\n TS[Time Series]\n Report[Report Generation]\n end\n\n subgraph \"Data Layer\"\n Cache[Results Cache]\n Storage[Secure Storage]\n Encrypt[Encryption Layer]\n end\n\n CLI --> LB\n Claude --> LB\n Cursor --> LB\n MCP --> LB\n\n LB --> Auth\n Auth --> Rate\n Rate --> Router\n\n Router --> Queue\n Queue --> Workers\n Workers --> Docker\n\n Docker --> Stats\n Docker --> ML\n Docker --> TS\n\n Stats --> Report\n ML --> Report\n TS --> Report\n\n Report --> Cache\n Cache --> Storage\n Storage --> Encrypt\n\n style Auth fill:#e8f5e9\n style Docker fill:#fff3e0\n style Report fill:#e3f2fd\n```\n\n## Performance\n\n- **Dataset Size**: Handles large datasets\n- **Processing Time**: Fast cloud-based processing\n- **Secure Infrastructure**: Isolated Docker containers\n- **API Access**: RESTful API with authentication\n\n## Getting Started\n\n[**Visit our website for pricing and signup →**](https://mcpanalytics.ai)\n\n## Documentation\n\n- [**Quick Start Guide**](https://github.com/embeddedlayers/mcp-analytics/blob/main/docs/quickstart.md) - Get running in under a minute\n- [**Architecture**](https://github.com/embeddedlayers/mcp-analytics/blob/main/docs/ARCHITECTURE.md) - How the platform works\n- [**Connectors**](https://github.com/embeddedlayers/mcp-analytics/blob/main/docs/connectors.md) - GA4, GSC, and CSV data sources\n- [**Pricing**](https://github.com/embeddedlayers/mcp-analytics/blob/main/docs/pricing.md) - Plans and limits\n- [**Security**](https://github.com/embeddedlayers/mcp-analytics/blob/main/SECURITY.md) - Security & compliance details\n- [**API Reference**](https://api.mcpanalytics.ai/docs) - Complete API documentation\n- [**Tutorials**](https://mcpanalytics.ai/tutorials) - Step-by-step guides\n\n## Support\n\n- **Issues**: [GitHub Issues](https://github.com/embeddedlayers/mcp-analytics/issues)\n- **Email**: support@mcpanalytics.ai\n- **Docs**: [mcpanalytics.ai/docs](https://mcpanalytics.ai/docs)\n- **Enterprise**: sales@mcpanalytics.ai\n\n## Comparison with Other MCP Servers\n\n| Feature | MCP Analytics | Google Analytics MCP | PostgreSQL MCP | Filesystem MCP |\n|---------|--------------|---------------------|----------------|----------------|\n| **Use Case** | Statistical Analysis | Web Metrics | Database Queries | File Access |\n| **Setup Time** | 30 seconds | OAuth + Config | Connection string | Path config |\n| **Data Sources** | Any CSV/JSON/URL | GA4 Only | PostgreSQL Only | Local files |\n| **Analysis Tools** | Full Suite | GA4 Metrics | SQL Only | Read/Write |\n| **Machine Learning** | ✅ Full Suite | ❌ | ❌ | ❌ |\n| **Visualizations** | ✅ Interactive | ✅ Dashboards | ❌ | ❌ |\n| **Shareable Reports** | ✅ | ❌ | ❌ | ❌ |\n\n[**Detailed comparison →**](https://mcpanalytics.ai/comparisons)\n\n## About MCP Analytics\n\nMCP 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.\n\n## Testing & Support\n\n### Testing Your Connection\n\nAfter 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.\n\n```bash\n# Test the stdio proxy directly:\nMCP_ANALYTICS_API_KEY=mcp_your_key npx -y @mcp-analytics/mcp-analytics\n# Should output: \"[mcp-analytics] Connected to https://api.mcpanalytics.ai. 19 tools available.\"\n```\n\n### Troubleshooting\n\nIf MCP Analytics doesn't appear after installation:\n1. Ensure your config file is valid JSON\n2. Restart your MCP client completely\n3. Verify your API key starts with `mcp_`\n4. Check the client's developer console for errors\n5. Try running the npx command in a terminal to see errors\n\nFor support: support@mcpanalytics.ai\n\n## Contributing\n\nWhile the core server is proprietary, we welcome contributions to:\n\n- Documentation improvements\n- Example notebooks and use cases\n- Bug reports and feature requests\n- Community tools and integrations\n\nSee [CONTRIBUTING.md](https://github.com/embeddedlayers/mcp-analytics/blob/main/CONTRIBUTING.md) for guidelines.\n\n## License\n\nCopyright © 2025 PeopleDrivenAI LLC. All Rights Reserved.\n\nMCP Analytics is a product of PeopleDrivenAI LLC.\n\nThis is commercial software. Use of the MCP Analytics service is subject to our:\n- [Terms of Service](https://mcpanalytics.ai/terms)\n- [Privacy Policy](https://mcpanalytics.ai/privacy)\n- [Acceptable Use Policy](https://mcpanalytics.ai/aup)\n\n---\n\n

\n\n**Ready to transform your data analysis workflow?**\n\n[**Get Started Free**](https://mcpanalytics.ai/signup) | [**Read Docs**](https://mcpanalytics.ai/docs) | [**View Demo**](https://mcpanalytics.ai/demo)\n\nBuilt by [MCP Analytics](https://mcpanalytics.ai) | Powered by R & Python\n\n

\n\n---\n\nIf MCP Analytics saves you time, a ⭐ on GitHub helps others find it.\n\n**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`\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays",0,"categoryDisplayName","Data & Analytics","upstream",{"_95":96,"_97":98,"_99":100,"_101":102,"_103":104},"requiredSecrets",[],"tags",[],"assets",[],"stats",{"_93":80,"_94":80},"ratingAvg","reviewCount","topics",[105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"license","MIT","language","JavaScript","registryName","ai.mcpanalytics/analytics","homepage","https://mcpanalytics.ai","ai-agents","ai-tools","analytics","anthropic","business-intelligence","claude","csv-analysis","cursor","data-analytics","data-science","interactive-reports","llm-tools","machine-learning","mcp","mcp-server","model-context-protocol","no-code","shopify-analytics","statistics","stripe-analytics","ENV",{"_127":128,"_129":130,"_131":132},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]

\n\nStatistical Methods\n- Regression Analysis\n- Advanced Modeling\n- Hypothesis Testing\n- Survival Analysis\n- Bayesian Methods\n\n	\n\nMachine Learning\n- Ensemble Methods\n- Boosting Algorithms\n- Neural Networks\n- Clustering\n- Dimensionality Reduction\n\n
\n\nTime Series\n- Forecasting\n- Seasonal Analysis\n- Trend Detection\n- Multivariate Models\n- Causal Analysis\n\n	\n\nBusiness Analytics\n- Customer Analytics\n- Market Analysis\n- Pricing Models\n- Predictive Analytics\n- Experimental Design\n\n