Ai-SparkBLOCK

This project implements a Model Context Protocol (MCP) server and client for optimizing Apache Spark code. The system provides intelligent code optimization suggestions and performance analysis through a client-server architecture.

How It Works

Code Optimization Workflow

graph TB
subgraph Input
A[Input PySpark Code] --> |spark_code_input.py| B[run_client.py]
end

subgraph MCP Client
B --> |Async HTTP| C[SparkMCPClient]
C --> |Protocol Handler| D[Tools Interface]
end

subgraph MCP Server
E[run_server.py] --> F[SparkMCPServer]
F --> |Tool Registry| G[optimize_spark_code]
F --> |Tool Registry| H[analyze_performance]
F --> |Protocol Handler| I[Claude AI Integration]
end

subgraph Resources
I --> |Code Analysis| J[Claude AI Model]
J --> |Optimization| K[Optimized Code Generation]
K --> |Validation| L[PySpark Runtime]
end

subgraph Output
M[optimized_spark_code.py]
N[performance_analysis.md]
end

D --> |MCP Request| F
G --> |Generate| M
H --> |Generate| N

classDef client fill:#e1f5fe,stroke:#01579b
classDef server fill:#f3e5f5,stroke:#4a148c
classDef resource fill:#e8f5e9,stroke:#1b5e20
classDef output fill:#fff3e0,stroke:#e65100

class A,B,C,D client
class E,F,G,H,I server
class J,K,L resource
class M,N,O output

Component Details

1. Input Layer
2. spark_code_input.py: Source PySpark code for optimization
3. run_client.py: Client startup and configuration

1. MCP Client Layer
2. Tools Interface: Protocol-compliant tool invocation

1. MCP Server Layer
2. run_server.py: Server initialization
3. Tool Registry: Optimization and analysis tools
4. Protocol Handler: MCP request/response management

1. Resource Layer
2. Claude AI: Code analysis and optimization
3. PySpark Runtime: Code e