Developer · Local MCP server
Spark SQL
Query Spark SQL clusters via Thrift/HiveServer2. Works with Spark, EMR, Hive, Impala.
What the MCP Registry states
The entry as published to the official MCP Registry (read 2026-10-04), latest version.
- Registry name
io.github.aidancorrell/spark-sql-mcp-server- Version
- 0.1.2
- Status
- Active
- Category
- developer
- Transport
- stdio (local process)
- Package
- PyPI
- Published
- 2026-02-08
- Updated
- 2026-02-08
- Publisher
- aidancorrell (GitHub)
- Repository
- github.com/aidancorrell/spark-sql-mcp-server
- Source
- Registry API entry
Packages
| Registry | Package | Version | Transport |
|---|---|---|---|
| PyPI | spark-sql-mcp-server | 0.1.2 | stdio |
How to connect Spark SQL
Spark SQL runs locally from a Python package published to PyPI: spark-sql-mcp-server version 0.1.2. It speaks MCP over stdio, so the client starts it as a program and talks to it through standard input and output. It needs Python; clients usually start it with uvx (from uv) or after pip install — the usual command is uvx spark-sql-mcp-server. It reads these environment variables: SPARK_HOST (required), SPARK_PORT, SPARK_DATABASE, SPARK_AUTH, SPARK_USERNAME, SPARK_PASSWORD (secret) and SPARK_KERBEROS_SERVICE_NAME; set them in the client's configuration for this server.
In the mcpServers JSON format that many desktop and editor MCP clients read, the entry looks like this (placeholders in angle brackets):
{
"mcpServers": {
"spark-sql-mcp-server": {
"command": "uvx",
"args": [
"spark-sql-mcp-server"
],
"env": {
"SPARK_HOST": "<value>",
"SPARK_PORT": "<value>",
"SPARK_DATABASE": "<value>",
"SPARK_AUTH": "<value>",
"SPARK_USERNAME": "<value>",
"SPARK_PASSWORD": "<secret>",
"SPARK_KERBEROS_SERVICE_NAME": "<value>"
}
}
}
}Derived from the registry entry, not tested here. What the server does, and on what terms, is set by its publisher; check its repository or website before giving it access to your accounts or files. How to add an MCP server to an assistant · Before you connect
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