@vgiri2015
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"ai-spark-mcp-server": {
"command": "python",
"args": [
"v1/run_server.py"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Spark MCP (Model Context Protocol) Optimizer is an MCP server and client that optimizes Apache Spark code by integrating Claude AI for intelligent code suggestions and performance analysis. It is designed for developers who want to improve PySpark code efficiency through a standardized protocol.
Install dependencies (pip install -r requirements.txt), place your PySpark code in input/spark_code_input.py, start the server with python v1/run_server.py, then run the client with python v1/run_client.py. Optionally run python v1/run_optimized.py to execute and compare original and optimized code.
MCP is a standardized protocol for AI model interactions, providing pre-built client libraries, automatic validation, result persistence, and context-aware optimization. Compared to direct Claude AI calls, it reduces custom integration and manual handling.
Python 3.8+, PySpark 3.2.0+, and an Anthropic API Key for Claude AI.
The server outputs `output/optimized_spark_example.py` (optimized code with comments) and `output/performance_analysis.md` (detailed performance comparison). Running `run_optimized.py` updates the analysis with execution metrics.
The user submits PySpark code, the MCP client sends it to the server, which uses Claude AI to analyze and generate optimizations. The optimized code is then validated against the PySpark runtime, and a performance analysis is produced.
Input code is read from `input/spark_code_input.py`; output files are written to the `output/` directory. The Claude AI analysis is performed via the Anthropic API, and the PySpark runtime executes locally.
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