mcp-apache-spark-history-server
Verified Safeby kubeflow
Overview
Connect AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring.
Installation
uvx --from mcp-apache-spark-history-server spark-mcpEnvironment Variables
- SHS_MCP_CONFIG
- SHS_MCP_PORT
- SHS_MCP_DEBUG
- SHS_MCP_ADDRESS
- SHS_MCP_TRANSPORT
- SHS_SERVERS_*_URL
- SHS_SERVERS_*_AUTH_USERNAME
- SHS_SERVERS_*_AUTH_PASSWORD
- SHS_SERVERS_*_AUTH_TOKEN
- SHS_SERVERS_*_VERIFY_SSL
- SHS_SERVERS_*_TIMEOUT
- SHS_SERVERS_*_EMR_CLUSTER_ARN
- SHS_SERVERS_*_INCLUDE_PLAN_DESCRIPTION
Security Notes
The project demonstrates a strong commitment to security with a dedicated `SECURITY.md`, a pre-commit hook to detect hardcoded credentials (`check-config-security.py`), and a configuration system that encourages environment variables for sensitive data. The use of Playwright for HTML rendering introduces a potential attack surface if the target Spark UI is compromised, but this is an inherent risk of browser automation and is generally mitigated by trusting the source (Spark History Server). Boto3 usage for EMR assumes secure AWS credential management in the execution environment. Overall, very good security practices for the technology stack.
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