mcp-server-chart vs mcp-apache-spark-history-server
Side-by-side comparison to help you pick between these two MCP servers.
mcp-server-chart by antvis | mcp-apache-spark-history-server by kubeflow | |
|---|---|---|
| Stars | ★ 4,068 | ★ 170 |
| 30d uses | 10,239 | — |
| Score | 84 | 50 |
| Official | — | — |
| Categories | AI / LLM ToolsDeveloper ToolsProductivity | Developer ToolsMonitoringAI / LLM Tools |
| Language | TypeScript | Python |
| Last commit | this month | this month |
mcp-server-chart · Summary
A TypeScript MCP server for generating 26+ visualization charts using AntV, supporting multiple chart types and deployment options.
mcp-apache-spark-history-server · Summary
MCP Server connecting AI agents to Apache Spark History Server for job analysis and performance monitoring.
mcp-server-chart · Use cases
- Data analysts creating visual reports from datasets
- AI assistants generating custom charts based on user requests
- Web applications embedding visualization capabilities via HTTP API
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
mcp-server-chart · Install
Installation
Install globally:
npm install -g @antv/mcp-server-chartFor Desktop Apps (e.g., Claude Desktop, VSCode):
{
"mcpServers": {
"mcp-server-chart": {
"command": "npx",
"args": ["-y", "@antv/mcp-server-chart"]
}
}
}For Windows:
{
"mcpServers": {
"mcp-server-chart": {
"command": "cmd",
"args": ["/c", "npx", "-y", "@antv/mcp-server-chart"]
}
}
}mcp-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": []
}
}
}