{"slug": "show-hn-prodata-ai-14-mcp-tools-for-automated-data-science", "title": "Show HN: ProData AI – 14 MCP tools for automated data science", "summary": "ProData AI, a professional automated data science platform integrated with Claude's Model Context Protocol, launched with 14 tools for tasks including AutoML training, time series forecasting, and report generation. The subscription-based service costs $9 per month and targets data analysts, engineers, and researchers seeking no-code data science capabilities.", "body_md": "# ProData AI\n\nProfessional data analysis tool integrated with Claude's Model Context Protocol, featuring AutoML training, time series forecasting, dataset analysis, feature importance, and report generation.\n\n## How to pay\n\n### Subscribe\n\n$9/month\n\nPredictable monthly cost with included usage. Best for steady, high-volume traffic.\n\n- Unlimited tools within plan limits\n- One API key, billed once a month\n- Cancel any time\n\n[Overview](#overview)\n\nProData AI is a professional-grade automated data science platform integrated with Claude's Model Context Protocol. It delivers a complete end-to-end data pipeline — from raw CSV to cleaned data, ML models, forecasts, anomaly detection, clustering, correlation analysis, SQL generation, interactive dashboards, and AI-powered explanations — all in one server with 14 tools. No code required.\n\n[Key Capabilities](#key-capabilities)\n\n- analyze_dataset_tool: Performs full statistical profiling — mean, median, std, missing values, duplicates, and data quality score.\n- train_automl_models_tool: Auto-trains and compares 6 ML models, returns the best performer with R² or accuracy score and feature importances.\n- forecast_timeseries_tool: Prophet-powered time series forecasting with confidence intervals and MAPE validation score.\n- get_feature_importance_tool: Identifies and ranks the top features driving your target variable using Random Forest.\n- generate_report_tool: Compiles stats, ML results, data quality assessment, and recommendations into a comprehensive report.\n- clean_dataset_tool: Automatically handles missing values, duplicates, whitespace, and outliers — returns a cleaned CSV with a full change log.\n- detect_anomalies_tool: Flags outlier rows using Isolation Forest, Z-score, or IQR — returns anomaly scores and a clean CSV with anomalies removed.\n- compare_datasets_tool: Side-by-side comparison of two CSVs — schema diff, statistical shifts, distribution changes, and an overall similarity verdict.\n- cluster_data_tool: K-Means segmentation returning cluster profiles, sizes, and top distinguishing features. Ideal for customer segmentation.\n- correlation_analysis_tool: Computes full correlation matrix with p-values, top correlated pairs, and multicollinearity warnings.\n- explain_model_tool: Claude-powered plain-English explanation of ML results with business insights and actionable recommendations.\n- generate_dashboard_tool: Returns a self-contained interactive HTML dashboard with KPI cards, line, bar, scatter, and doughnut charts.\n- suggest_visualizations_tool: Analyzes column types and recommends the best chart types with rationale and column mappings.\n- generate_sql_tool: Claude-powered natural language to SQL — describe what you want in plain English, get a ready-to-run query back.\n\n[Use Cases](#use-cases)\n\n- A supply chain manager uses forecast_timeseries_tool to predict inventory demand for the next quarter based on historical sales data.\n- A fraud analyst uses detect_anomalies_tool with Isolation Forest to flag suspicious transactions in a financial dataset.\n- A marketing analyst uses cluster_data_tool to segment customers by behavior and spending patterns.\n- A data engineer uses clean_dataset_tool to fix missing values and remove duplicates before loading data into a pipeline.\n- A business analyst uses explain_model_tool to get a plain-English ML summary for a boardroom presentation.\n- A researcher uses compare_datasets_tool to detect data drift between last month's and this month's dataset before retraining a model.\n- A developer uses generate_sql_tool to instantly convert plain English questions into ready-to-run SQL queries.\n- A BI team uses generate_dashboard_tool to get an interactive HTML dashboard from any CSV in seconds.\n\n[Who This Is For](#who-this-is-for)\n\nThis server is designed for data analysts, business analysts, data engineers, software developers, and technical researchers who need professional-grade data science outputs without building custom ML pipelines from scratch. Whether you need to clean data, train ML models, forecast trends, detect anomalies, segment customers, or generate dashboards — ProData AI handles the full pipeline in one MCP server. It is ideal for users familiar with CSV data structures who require immediate, evidence-based insights to inform their decision-making process. Compatible with Claude Desktop, Cursor, VS Code, Windsurf, and any MCP-compliant client.", "url": "https://wpnews.pro/news/show-hn-prodata-ai-14-mcp-tools-for-automated-data-science", "canonical_source": "https://mcpize.com/mcp/prodata-ai", "published_at": "2026-06-16 09:32:37+00:00", "updated_at": "2026-06-16 09:48:38.915603+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence", "ai-tools"], "entities": ["ProData AI", "Claude", "Model Context Protocol", "Prophet", "Random Forest", "Isolation Forest", "K-Means", "SQL"], "alternates": {"html": "https://wpnews.pro/news/show-hn-prodata-ai-14-mcp-tools-for-automated-data-science", "markdown": "https://wpnews.pro/news/show-hn-prodata-ai-14-mcp-tools-for-automated-data-science.md", "text": "https://wpnews.pro/news/show-hn-prodata-ai-14-mcp-tools-for-automated-data-science.txt", "jsonld": "https://wpnews.pro/news/show-hn-prodata-ai-14-mcp-tools-for-automated-data-science.jsonld"}}