local-spark-mcp
Provides a stateful local Spark session for running PySpark and SQL cells, enabling local data exploration before deploying to Microsoft Fabric.
README
local-spark-mcp
An MCP server that gives an agent a stateful local Spark session to work in — a Jupyter-notebook-shaped surface with the UI stripped away. The agent runs PySpark "cells" against a long-lived session (state persists across calls), runs SQL and gets rows back, and manages the runtime through tools.
The purpose is local exploration in service of authoring PySpark notebooks that will run on Microsoft Fabric: figure things out locally against the same OneLake Delta data, then hand the honed code to the user as a notebook to run on Fabric with a reasonably similar outcome — no cloud compute burned while exploring.
Status
Milestones A, B.1, B.2 complete and validated live. See CLAUDE.md for the
architecture and the locked design decisions.
Running it (via uvx, from GitHub)
No clone or build needed — uvx installs and runs it in an ephemeral
environment. Register it as an MCP server in Claude Code (.mcp.json):
{
"mcpServers": {
"local-spark": {
"command": "uvx",
"args": ["--from", "git+https://github.com/methodify/local-spark-mcp", "local-spark-mcp"],
"env": { "LOCAL_SPARK_WORKSPACE_NAME": "Data Warehouse" }
}
}
}
Prerequisites on the host:
- Java 17 for Spark 3.5 (the server prefers a vfox-managed JDK 17, else
JAVA_HOME; or setruntime.java_home/LOCAL_SPARK_JAVA_HOME). System Java 21 will not work. az login— OneLake/Fabric auth is ambient viaDefaultAzureCredential.
The prebuilt OneLake token-provider jar ships inside the package, so Fabric mode
works out of the box (no sbt needed). First run downloads PySpark/Delta jars and
is slow; subsequent runs reuse the cached environment. Use --refresh to pick up
a new commit: uvx --refresh --from git+https://github.com/methodify/local-spark-mcp local-spark-mcp.
Configuration
Configuration lives in a local-spark.toml file in the working directory (see
local-spark.example.toml), discovered by walking up from where the server is
launched. Environment variables (LOCAL_SPARK_*) override individual settings —
convenient in the MCP env block above when you don't want a file. With no
workspace configured the server runs local-only (no Fabric). Auth is ambient via
az login, so nothing in the config is secret.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.