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    Enables analysis of JVM thread dumps to detect deadlocks, CPU spikes, pool exhaustion, and virtual thread pinning, returning a health score and interactive report link.
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    MIT
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    MCP server that gives AI agents access to your application's OpenTelemetry traces for querying, analysis, and debugging.
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    MCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.
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    A Model Context Protocol (MCP) server for PostgreSQL that provides safe, structured access to your database for AI assistants, enabling health checks, index tuning, lock analysis, and more.
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    MIT
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    Provides seamless integration between AI assistants and Prometheus, enabling natural language interactions with your monitoring infrastructure. This server allows for effortless querying, discovery, and analysis of metrics.
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    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.
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    MIT
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    Enables AI-powered analytics for OpenReplay user sessions through natural language queries. Supports session search, user journey analysis, error tracking, performance metrics, and funnel analysis to understand user behavior patterns.
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    Enables log analysis, searching, counting, and system metrics retrieval (CPU, memory, disk) via natural language, using 5 tools that can be integrated with Claude Code.
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    Enables interaction with Datadog's monitoring platform to search logs, search trace spans, and perform trace span aggregation for analysis.
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    A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.
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    Apache 2.0
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    Connects AI assistants to cloud and SaaS logs via Tailpipe, enabling natural language querying and analysis of log data.
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    Apache 2.0
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    Enables data observability operations with the Sifflet platform. Supports exploring assets, monitors, incidents, generating monitor-as-code YAML from descriptions, and performing impact analysis.
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    7
    MIT