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Closes #900

Summary

Implements a comprehensive MCP (Model Context Protocol) data analysis
server as specified in Issue #900. This server provides advanced data
science capabilities through 7 standardized MCP tools, enabling AI
applications to perform sophisticated statistical analysis,
visualization, and data transformation workflows.

Key Features Implemented:

• 7 MCP Tools: load_dataset, analyze_dataset, statistical_test,
  create_visualization, transform_data, time_series_analysis,
  query_data
• Multi-format Data Loading: Support for CSV, JSON, Parquet, SQL,
  Excel with validation and sampling
• Statistical Analysis Engine: Descriptive statistics, hypothesis
  testing (t-tests, ANOVA, chi-square, etc.), correlation analysis
• Visualization Capabilities: Multiple plot types using matplotlib,
  seaborn, and plotly
• Time Series Analysis: Trend detection, seasonality analysis,
  forecasting
• Data Transformation Pipeline: 14+ transformation types including
  cleaning, scaling, encoding
• SQL-like Query Engine: Pandas-based querying with familiar syntax
• Performance Optimization: In-memory caching, LRU eviction, memory
  monitoring

Architecture:

• Modular design with clear separation of concerns
• Comprehensive error handling and input validation
• Security features including data source validation and query complexity
  limits
• Extensive test coverage (unit, integration, performance tests)
• Complete documentation with examples and configuration guides

Testing:

• ✅ Unit tests for all core components
• ✅ Integration tests for MCP tool workflows
• ✅ Performance tests for large dataset handling
• ✅ Example workflows demonstrating all capabilities