The same API the agents use.
Everything the MCP tools do is plain REST underneath: datasets, analyses, builds, reports. API key, JSON in and out, and every report has its own URL.
Two Ways to Connect
Use the MCP protocol for conversational AI workflows, or call the REST API directly from your application.
MCP Protocol
Connect from Claude Desktop, Cursor, Windsurf, or any MCP-compatible client. Natural language interface with automatic tool selection.
- Zero-config setup via npx
- API key authentication (OAuth2 is experimental)
- Semantic tool discovery built in
- Interactive HTML reports returned inline
- Upload CSV, connect live data sources
- Works with any MCP client
REST API
Direct HTTP endpoints for programmatic access. Build custom pipelines, automate recurring analyses, or integrate into existing applications.
- Standard REST endpoints
- Bearer token authentication
- Upload, discover, run, retrieve
- JSON request/response format
- Rate limits by subscription tier
- Async execution for long analyses
How It Works
Four steps from raw data to interactive report. The platform handles method selection, execution, and visualization.
Upload Data
Send a CSV file or connect a live data source (Google Analytics, Search Console).
Discover Tools
Describe what you want to learn. Semantic matching finds the best statistical method for your data structure and question.
Run Analysis
Run the analysis as real R on your data, with automatic error recovery.
Get Results
Receive an interactive HTML report with charts, statistical tables, and AI-narrated insights. Export as PDF. See a sample report.
Connect in Minutes
Add MCP Analytics to your client or call the API directly.
// Add to Claude Desktop MCP settings // claude_desktop_config.json { "mcpServers": { "mcpanalytics": { "command": "npx", "args": ["@mcp-analytics/mcp-analytics"] } } } // Restart Claude Desktop, then: // "Upload sales.csv and find which products drive the most revenue" // "Run churn prediction on my customer data" // "What statistical test should I use for this survey data?"
// Add to Cursor or Windsurf MCP settings // Settings > MCP Servers > Add Server { "mcpServers": { "mcpanalytics": { "command": "npx", "args": ["@mcp-analytics/mcp-analytics"], "env": { "MCP_ANALYTICS_API_KEY": "https://api.mcpanalytics.ai" } } } } // Experimental OAuth2: a browser login may open on first use // If it does not complete, use an API key instead
# 1. Upload a dataset curl -X POST https://api.mcpanalytics.ai/api/v1/myaccount/datasets/upload/file \ -H "Authorization: Bearer $TOKEN" \ -F "file=@sales_data.csv" # Response: { "dataset_id": "uuid://…", "columns": [...] } # 2. Discover compatible analysis tools curl -X POST https://api.mcpanalytics.ai/discover/tools \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"dataset_id": "uuid://…", "query": "revenue trends"}' # 3. Run the analysis curl -X POST https://api.mcpanalytics.ai/tools/run \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"tool": "time_series_arima", "dataset_id": "uuid://…", "column_mapping": {"date": "order_date", "value": "revenue"}}'
# Python: using requests import requests API = "https://api.mcpanalytics.ai" headers = {"Authorization": f"Bearer {token}"} # Upload dataset upload = requests.post( f"{API}/datasets/upload", headers=headers, files={"file": open("sales_data.csv", "rb")} ) dataset_id = upload.json()["dataset_id"] # Discover tools for this dataset tools = requests.post( f"{API}/discover/tools", headers=headers, json={"dataset_id": dataset_id, "query": "revenue forecast"} ).json() # Run the top-matched analysis result = requests.post( f"{API}/tools/run", headers=headers, json={ "tool": tools["matches"][0]["tool_name"], "dataset_id": dataset_id, "column_mapping": {"date": "order_date", "value": "revenue"} } ).json() # result["report_url"] → interactive HTML report # result["cards"] → individual chart/table components
What You Can Build
Every MCP tool is callable from any connected client. Here's what's available.
datasets_upload
Upload CSV files for analysis. Auto-detects column types, distributions, and temporal signals.
datasets_list
Browse your uploaded datasets with metadata, column schemas, and row counts.
discover_tools
Semantic tool matching. Describe what you want to learn: the platform finds the best statistical method.
run_analysis
Execute an analysis. Returns interactive HTML report with charts, tables, and AI insights.
reports_list
Browse completed analyses. Filter by dataset, tool, date, or status.
connectors_query
Query live data from Google Analytics 4 and Search Console (live today; more sources on the roadmap) without CSV export.
The Full Analysis Toolset
From basic descriptive stats to advanced machine learning. The semantic discovery system matches your data to the right method automatically.
Regression
Linear, ridge, lasso, elastic net, logistic, polynomial. Feature selection and coefficient interpretation.
Clustering
K-means, DBSCAN, hierarchical. Customer segmentation, anomaly grouping, pattern discovery.
Time Series
ARIMA, Prophet, trend decomposition. Revenue forecasting, demand planning, seasonality detection.
Hypothesis Testing
t-test, ANOVA, chi-square, Mann-Whitney, Kruskal-Wallis. A/B testing with proper corrections.
Anomaly Detection
Isolation forest, one-class SVM. Fraud detection, quality control, outlier identification.
Customer Analytics
RFM segmentation, churn prediction, cohort retention, lifetime value, AOV analysis.
Auth & Rate Limits
API keys for MCP clients and Bearer tokens for direct API access; OAuth2 is experimental. Rate limits scale with your plan.
Authentication
All requests require authentication. MCP clients use an API key (OAuth2 via browser login is experimental). API clients use Bearer tokens.
- API key (MCP clients); OAuth2 via Auth0 is experimental
- Bearer tokens (REST API)
Rate Limits
Limits vary by subscription tier. Rate limit headers included in every API response for client backoff.
- Free and paid plans: each tier has its own limits
- Business/Enterprise: custom limits, priority queue
- 429 responses include retry-after
Common Questions
How do I connect MCP Analytics to Claude Desktop?
Add MCP Analytics as an MCP server in your Claude Desktop configuration. After signing up, you'll receive connection credentials. Add the server config to your MCP settings, restart Claude Desktop, and you can immediately start analyzing data through conversation.
What MCP clients are supported?
MCP Analytics works with any MCP-compatible client including Claude Desktop, Cursor, Windsurf, and custom MCP clients. The server uses stdio transport via npx for easy setup with no local installation required beyond Node.js.
Do I need to know which statistical test to use?
No. The semantic tool discovery system analyzes your data structure and your question to automatically select an analysis suited to your question. You describe what you want to learn ("Why are customers leaving?" or "What drives revenue?") and the platform matches the right statistical method.
What data formats are supported?
CSV files. The platform also supports live data connections to Google Analytics 4 and Google Search Console, no CSV export required. Column types (numeric, categorical, datetime) are auto-detected.
Is my data secure?
All data is encrypted in transit.
Build on the server
The MCP server is public: tools, schemas, examples, and the roadmap. Wire it into your own agents, open an issue for an analysis you need, or tell us what to build next. When your team needs custom analytics on real data, we're here.
Where to start
One email with your data and your question. We take it from there.
MCP Analytics
Data science by email
Send us your data and your question, and we will reply.
Try it on your own data
30 days free.
Answers, analytics and custom analytics. No card, no call, nothing to install.
30 days free. →