ultimate_mcp_server vs mcp-apache-spark-history-server
Side-by-side comparison to help you pick between these two MCP servers.
ultimate_mcp_server by Dicklesworthstone | mcp-apache-spark-history-server by kubeflow | |
|---|---|---|
| Stars | ★ 149 | ★ 170 |
| 30d uses | — | — |
| Score | 85 | 50 |
| Official | — | — |
| Categories | AI / LLM ToolsBrowser AutomationFile System | Developer ToolsMonitoringAI / LLM Tools |
| Language | Python | Python |
| Last commit | 2 mo ago | this month |
ultimate_mcp_server · Summary
Comprehensive MCP server providing dozens of capabilities for AI agents including LLM delegation, browser automation, document processing, and cognitive memory systems.
mcp-apache-spark-history-server · Summary
MCP Server connecting AI agents to Apache Spark History Server for job analysis and performance monitoring.
ultimate_mcp_server · Use cases
- Complex document processing and analysis with OCR and structured data extraction
- Web automation and research across multiple sites with browser control
- Cost-optimized AI workflows through intelligent task delegation between models
mcp-apache-spark-history-server · Use cases
- AI agents investigating failed or slow Spark applications using natural language queries
- Comparing performance metrics between different Spark job runs to identify regressions
- Automating Spark job monitoring and alerting through integration with AI agents
ultimate_mcp_server · Install
Installation
- Clone the repository:
git clone https://github.com/Dicklesworthstone/ultimate_mcp_server.git
cd ultimate_mcp_server- Install dependencies:
pip install -e .- For Claude Desktop integration, add to your claude_desktop_config.json:
{
"mcpServers": {
"ultimate-mcp": {
"command": "python",
"args": ["-m", "ultimate_mcp_server"],
"env": {
"PYTHONPATH": "."
}
}
}
}- Run the server:
python -m ultimate_mcp_servermcp-apache-spark-history-server · Install
Install with pip:
pip install mcp-apache-spark-history-server
spark-mcpRun directly with uvx (no install needed):
uvx --from mcp-apache-spark-history-server spark-mcpConfiguration via config.yaml (supports multiple servers):
servers:
local:
default: true
url: "http://your-spark-history-server:18080"
auth:
username: "user"
password: "pass"
mcp:
transports:
- streamable-http
port: "18888"Claude Desktop configuration:
{
"mcpServers": {
"spark": {
"command": "spark-mcp",
"args": []
}
}
}