pyspark-mcp 0.0.6

Minimal PySpark MCP server inspired by LakeSail

hi# PySpark MCP Server

Description

PySpark MCP Server is a lightweight server implementation of Model Context Protocol (MCP) for Apache Spark.

The primary purpose of this MCP server is to facilitate query optimization using AI systems. It provides both logical and physical query plans from Spark to AI systems for analysis, along with additional query plan information. Furthermore, the server exposes catalog and table information, enabling data discovery capabilities in data lakes powered by Spark.

Quick Start

Installation

pip install pyspark-mcp

Running the Server

After installation, use the pyspark-mcp command to start the server:

pyspark-mcp --master "local[*]" --host 127.0.0.1 --port 8090

The CLI automatically handles spark-submit configuration. All standard spark-submit options are supported:

# With additional Spark configuration
pyspark-mcp --master "local[*]" --conf spark.driver.memory=4g

# YARN cluster mode
pyspark-mcp --master yarn --deploy-mode client --num-executors 4

# With additional JARs
pyspark-mcp --master "local[*]" --jars /path/to/connector.jar

# Preview the spark-submit command without running
pyspark-mcp --master "local[*]" --dry-run

# With GraphFrames package
pyspark-mcp --master "local[*]" --packages io.graphframes:graphframes-spark3_2.12:0.10.1

CLI Options

Option	Default	Description
--master	local[*]	Spark master URL
--host	127.0.0.1	MCP server host address
--port	8090	MCP server port number
--spark-submit	spark-submit	Path to spark-submit executable
--dry-run	-	Print command without executing

All spark-submit options (--conf, --jars, --packages, --executor-memory, etc.) are passed through automatically.

Adding the running MCP to the Claude-code

# Must run one server on a different port per Claude instance
claude mcp add --transport http pyspark-mcp http://127.0.0.1:8090/mcp

Dependencies

• Python >=3.11,<4.0
• fastmcp >= 2.10.6
• loguru
• pyspark >= 3.5

Bundled MCP tools

The following tools are included in the PySpark MCP Server:

MCP Tool	Description
Get the version of PySpark	Get the version number from the current PySpark Session
Get Analyzed Plan of the query	Extracts an analyzed logical plan from the provided SQL query
Get Optimized Plan of the query	Extracts an optimized logical plan from the provided SQL query
Get size estimation for the query results	Extracts a size and units from the query plan explain
Get tables from the query plan	Extracts all the tables (relations) from the query plan explain
Get the current Spark Catalog	Get the catalog that is the default one for the current SparkSession
Check does database exist	Check if the database with a given name exists in the current Catalog
Get the current default database	Get the current default database from the default Catalog
List all the databases in the current catalog	List all the available databases from the current Catalog
List available catalogs	List all the catalogs available in the current SparkSession
List tables in the current catalog	List all the available tables in the current Spark Catalog
Get a comment of the table	Extract comment of the table or returns an empty string
Get table schema	Get the spark schema of the table in the catalog
Returns a schema of the result of the SQL query	Run query, get the result, get the schema of the result and return a JSON-value of the schema
Read first N lines of the text file	Read the first N lines of the file as a plain text. Useful to determine the format