Pyspark Mcp
Convert SQL to PySpark, scaffold AWS Glue jobs, and tighten Spark code from the agent while building data backends.
Overview
PySpark MCP is a MCP server for the Build phase that helps agents convert SQL to PySpark, generate AWS Glue jobs, and optimize Spark code over stdio.
What is this MCP server?
- SQL to PySpark conversion for agent-assisted pipeline authoring
- AWS Glue job generation from prompts or specs
- Spark code optimization suggestions via pyspark-tools PyPI package v0.0.4
- stdio MCP transport through PyPI identifier pyspark-tools
- Focused on batch/ETL stacks rather than generic CRUD APIs
- PyPI package pyspark-tools version 0.0.4
- Capabilities: SQL→PySpark, Glue job generation, Spark optimization (3)
- Transport: stdio
What problem does it solve?
Writing and tuning PySpark and Glue jobs by hand slows solo builders who already use agents for application code.
Who is it for?
Indie data engineers and full-stack solos building AWS-centric ETL or analytics backends with MCP-enabled agents.
Skip if: Pure frontend products, non-Spark databases only, or teams without any Spark/Glue runtime to test generated code.
What do I get? / Deliverables
After install, your agent can draft PySpark transforms, Glue job scripts, and optimization passes you validate in your data environment.
- Draft PySpark from SQL specifications
- Scaffold AWS Glue job code for further hardening
- Agent-suggested Spark optimizations to benchmark in your cluster
Recommended MCP Servers
Journey fit
How it compares
Spark/Glue codegen MCP, not a warehouse admin GUI or generic SQL lint skill.
Common Questions / FAQ
Who is pyspark-mcp for?
Builders creating PySpark pipelines or AWS Glue jobs who want agent assistance through a stdio MCP Python package.
When should I use pyspark-mcp?
Use it during backend build when translating SQL logic to Spark, bootstrapping Glue jobs, or refactoring slow Spark code.
How do I add pyspark-mcp to my agent?
Install pyspark-tools from PyPI (v0.0.4), configure the stdio MCP server entry pointing at that package, and ensure Python dependencies for PySpark workflows are available locally.