"Assistance with Java development" matching MCP servers:

A comprehensive MCP toolkit for Java backend developers, providing 35 tools across 5 servers for database analysis, JVM diagnostics, migration assistance, Spring Boot monitoring, and Redis diagnostics.

Java profiling with AI agent integration. Profile CPU, memory, JDBC, HTTP and more.

A JVM monitoring and control platform server based on Arthas that provides a Python interface for monitoring and analyzing Java processes with capabilities for thread analysis, memory monitoring, and performance diagnostics.

Query Langfuse traces, schema and datasets, scores and metrics, debug exceptions, analyze sessions, and manage prompts. Full observability toolkit for LLM applications.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

Lets AI agents diagnose and debug live Java applications by auto-discovering JVMs and attaching Arthas to run diagnostic commands like thread, trace, watch, and ognl.

Enables real-time data streaming through Server-Sent Events with timestamp broadcasting and server monitoring capabilities. Optimized for deployment on Render.com with health checks and status endpoints.

MCP servers for real-time development monitoring and interactive pair programming.

Java diagnostics MCP server for LLM integration with Alibaba Arthas, enabling analysis and diagnosis of Java applications.

Enables AI-powered centralized log monitoring and Q\&A for GCP Java microservices, allowing log query, service health checks, and anomaly detection through MCP tools.

Aggregates multiple Arthas agents into a single MCP server, allowing Claude to diagnose any number of Java processes with just one MCP configuration.

Enables secure execution of Python code, SQL queries, and metric fetching through MCP with ephemeral Docker sandboxing.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

Enables AI assistants to manage remote servers via SSH with 14 commands for execution, file transfer, auditing, and monitoring.

AI development observability platform. It silently captures structured data from vibe coding sessions via MCP and codifies deviation patterns into project rules to make AI write better code. 100% local and zero runtime dependencies.

MCP server for profiling Java applications via JDK utilities (jcmd, jfr, jps). Enables AI assistants to diagnose performance, analyze threads, and inspect JFR recordings without manual CLI usage.

Enables tracking and analyzing development time through the Wakapi API. Provides tools to retrieve coding statistics, project details, leaderboards, and recent activity logs for productivity insights.

Provides AI coding agents with real-time visibility into local development runtime state, enabling them to tail application logs, inspect ports, monitor process metrics, and diagnose network errors.

A Model Context Protocol server that connects AI agents to live Java processes via Alibaba Arthas, enabling real-time diagnostics, debugging, and performance analysis without code changes.

Integrates Honeybadger error tracking with Cursor IDE, allowing developers to fetch, analyze, and troubleshoot application errors directly from their development environment.