mcp-apache-spark-history-server vs fastapi_mcp
mcp-apache-spark-history-server: The Kubeflow Spark History MCP Server bridges AI agents with Apache Spark infrastructure, enabling intelligent job analysis, performance monitoring, and failure investigation. It provides 18 specialized MCP tools for querying Spark History Server data, supporting multi-server configurations and AWS integrations.; fastapi_mcp: FastAPI-MCP allows you to expose your existing FastAPI endpoints as Model Context Protocol (MCP) tools. It offers native integration with FastAPI, including built-in authentication using existing dependencies, and preserves schemas and documentation.
TL;DR
Investigate why a Spark job is running slower than usual
- You already have a FastAPI application and want to expose its endpoints as MCP tools while reusing your existing FastAPI authentication dependencies and Swagger schemas.
- You want to mount the MCP server directly onto your ASGI app or deploy it separately under an MIT license.
- You are building a standalone MCP server from scratch rather than wrapping a FastAPI app; use FastMCP or the official MCP Python SDK.
- You need an actively updated repository (last push was November 2025).
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