mcp-claude-code

To implement a real-time data processing pipeline using MCP and Claude Code, we'll create a solution that ingests data from multiple sources, processes it, and outputs the results to a dashboard. Here's the implementation:

```python
import mcp
import claudecode as cc

# Initialize MCP client
mcp_client = mcp.Client(api_key='your_api_key')

# Define data sources
sources = [
    {'name': 'source1', 'type': 'api', 'url': 'https://api.example.com/data', 'params': {'format': 'json'}},
    {'name': 'source2', 'type': 'database', 'query': 'SELECT * FROM sales_data WHERE date > CURRENT_DATE - INTERVAL 
d
'}
]

# Create data processing pipeline
pipeline = cc.Pipeline(
    sources=sources,
    processors=[
        cc.Processor('filter', {'criteria': {'status': 'active'}}),
        cc.Processor('transform', {'operations': ['clean', 'normalize']}),
        cc.Processor('aggregate', {'by': 'region', 'metrics': ['sum', 'avg']})
    ],
    outputs=[
        {'name': 'dashboard', 'type': 'visualization', 'template': 'sales_dashboard'},
        {'name': 'storage', 'type': 'database', 'table': 'processed_data'}
    ]
)

# Deploy pipeline using MCP
mcp_client.deploy(pipeline, name='real_time_sales_pipeline', resources={'cpu': 4, 'memory': 8})
```

This implementation creates a real-time data processing pipeline that:
1. Ingests data from two sources (an API and a database)
2. Filters, cleans, normalizes, and aggregates the data
3. Outputs the results to a dashboard and stores them in a database
4. Is deployed using MCP with specified resources

The pipeline is optimized for low-latency processing and can handle high data volumes.