viirya opened a new pull request, #55648:
URL: https://github.com/apache/spark/pull/55648
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### What changes were proposed in this pull request?
Adds an Apache Spark MCP server that acts as a thin client over Spark
Connect, exposing Spark capabilities as MCP tools for LLM consumption. Catalog
browsing, SQL execution, and query plan tools are read-only by default.
Module layout (python/pyspark/sql/mcp/):
- server.py CLI entry point and MCP tool registration
- config.py ServerConfig dataclass (env + CLI sources)
- session.py Lazy SparkSession holder over Spark Connect
- safety.py Read-only SQL guardrail
- tools/registry.py Tool spec / handler abstraction
- tools/session.py get_session_info (with config redaction)
- tools/catalog.py list_catalogs, list_databases, list_tables,
describe_table
- tools/query.py list_functions, execute_sql, preview_table,
explain_query, analyze_query
Tool handlers are MCP-SDK-agnostic and Connect-import-free at module load
time, so the unit tests run without grpcio or the mcp SDK installed. 14 unit
tests in python/pyspark/sql/tests/mcp/test_mcp_tools.py exercise the full tool
surface against an in-memory fake session.
### Why are the changes needed?
LLM clients can already talk to MCP servers; Spark Connect already separates
client from cluster. This module connects the two: a Spark cluster shows up to
an LLM as a set of safe, paginated tools — `list_tables`, `describe_table`,
`execute_sql`, `explain_query`, etc.
### Does this PR introduce _any_ user-facing change?
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Yes. User can do Spark queries in LLMs like Claude Code using these MCP
tools.
### How was this patch tested?
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Unit test. Manual test in Claude Code.
### Was this patch authored or co-authored using generative AI tooling?
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Generated-by: Claude Code
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