# Kimi K2 Heavy Processor MCP

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Heavy computation and data processing MCP for Claude Desktop. Handle complex SQL operations, large-scale data transformations, and resilient batch processing with automatic retry mechanisms.

## 🌟 Features

- **SQL Processing**: Full SQLite support with complex queries
- **Batch Operations**: Process millions of records efficiently
- **Resilient Execution**: Automatic retry with exponential backoff
- **Data Pipelines**: ETL operations with streaming support
- **Memory Management**: Smart chunking for large datasets
- **Progress Tracking**: Real-time status updates
- **Error Recovery**: Checkpoint-based resumption

## 🚀 Core Capabilities

### SQL Operations
- Complex JOIN operations across multiple tables
- Window functions and CTEs
- Bulk inserts and updates
- Transaction management
- Index optimization

### Data Processing
- CSV/JSON/XML parsing and generation
- Data validation and cleansing
- Format conversions
- Aggregation pipelines
- Statistical computations

### Resilience Features
- Automatic retry on failure (3 attempts)
- Exponential backoff (1s, 2s, 4s)
- Transaction rollback on error
- Progress checkpointing
- Partial result recovery

## 📦 Installation

### Via NPM (Recommended)
```bash
npm install -g kimi-k2-heavy-processor-mcp
```

### Manual Installation
```bash
git clone https://github.com/justmy2satoshis/kimi-k2-heavy-processor-mcp.git
cd kimi-k2-heavy-processor-mcp
pip install -r requirements.txt
```

## 🔧 Configuration

Add to your Claude Desktop configuration file:

**Windows**: `%APPDATA%\Claude\claude_desktop_config.json`
**macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "kimi-k2-heavy-processor": {
      "command": "python",
      "args": ["C:\\path\\to\\kimi-k2-heavy-processor-mcp\\src\\server.py"],
      "env": {
        "DB_PATH": "C:\\Users\\username\\AppData\\Local\\kimi-k2\\data.db",
        "MAX_MEMORY_MB": "2048",
        "CHUNK_SIZE": "10000"
      }
    }
  }
}
```

## 📖 Usage Examples

### Execute SQL Query
```python
result = await execute_sql({
  "query": "SELECT * FROM users WHERE created_at > ?",
  "params": ["2024-01-01"],
  "database": "main.db"
})
```

### Batch Data Processing
```python
processed = await process_batch({
  "input_file": "data.csv",
  "operations": [
    {"type": "filter", "condition": "amount > 100"},
    {"type": "transform", "mapping": "amount * 1.1"},
    {"type": "aggregate", "group_by": "category"}
  ],
  "output_format": "json"
})
```

### Resilient Operation
```python
result = await resilient_execute({
  "operation": "complex_etl",
  "source": "raw_data.csv",
  "max_retries": 3,
  "checkpoint_interval": 1000
})
```

### Data Pipeline
```python
pipeline = await create_pipeline({
  "stages": [
    {"name": "extract", "source": "database"},
    {"name": "transform", "rules": "business_logic.json"},
    {"name": "load", "target": "warehouse.db"}
  ],
  "parallel": true
})
```

## 💡 Use Cases

### Data Analysis
- Large CSV file processing
- Statistical computations
- Data aggregation and grouping
- Time series analysis

### ETL Operations
- Database migrations
- Data warehouse loading
- Format conversions
- Data cleansing pipelines

### Batch Processing
- Bulk email processing
- Log file analysis
- Report generation
- Data validation

### SQL Operations
- Complex reporting queries
- Database maintenance
- Index optimization
- Performance analysis

## 🏗️ Architecture

```
kimi-k2-heavy-processor-mcp/
├── src/
│   ├── server.py           # Main MCP server
│   ├── sql_processor.py    # SQL execution engine
│   ├── batch_processor.py  # Batch operations
│   ├── resilient.py        # Retry mechanisms
│   └── pipeline.py         # Data pipelines
├── examples/               # Usage examples
├── tests/                  # Test suite
└── requirements.txt
```

## 📊 Performance Metrics

| Operation | Records/Second | Memory Usage |
|-----------|---------------|--------------|
| **CSV Read** | 100,000 | <500MB |
| **SQL INSERT** | 50,000 | <200MB |
| **JOIN Query** | 1M rows/sec | <1GB |
| **Aggregation** | 500,000 | <300MB |
| **Transform** | 75,000 | <400MB |

## 🧪 Testing

```bash
pytest tests/
```

Tests cover:
- SQL operation accuracy
- Retry mechanism validation
- Memory management
- Performance benchmarks
- Error recovery

## 🤝 Contributing

Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

### Priority Areas
1. Additional data formats
2. Performance optimizations
3. New SQL functions
4. Pipeline templates

## 🔒 Security

- SQL injection prevention
- Input sanitization
- Secure file operations
- Memory limit enforcement
- Process isolation

## 📝 License

MIT License - see [LICENSE](LICENSE) file for details

## 🙏 Acknowledgments

- Anthropic for Model Context Protocol
- SQLite team for embedded database
- Python community for data tools
- Contributors and testers

## 📧 Support

- **Issues**: [GitHub Issues](https://github.com/justmy2satoshis/kimi-k2-heavy-processor-mcp/issues)
- **Discussions**: [GitHub Discussions](https://github.com/justmy2satoshis/kimi-k2-heavy-processor-mcp/discussions)

## 🚦 Status

- ✅ Production Ready
- ✅ Resilient execution
- ✅ Large-scale processing
- ✅ Comprehensive testing
- ✅ Claude Desktop compatible

## ⚡ Quick Start

```python
# 1. Load CSV data
await load_csv("sales_data.csv", "sales_table")

# 2. Process with SQL
await execute_sql("""
  SELECT
    category,
    SUM(amount) as total,
    AVG(amount) as average
  FROM sales_table
  GROUP BY category
  HAVING total > 10000
""")

# 3. Export results
await export_results("summary.json", format="json")
```

---

**Note**: Requires Claude Desktop with MCP support enabled.

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