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    Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.
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    Apache 2.0
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    Enables interaction with Gremlin's reliability management APIs to monitor service dependencies, run reliability experiments, generate reliability reports, and manage chaos engineering tests through natural language queries.
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    Apache 2.0
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    A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.
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    Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.
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    MIT
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    Provides read-only access to Jaeger distributed tracing data through the Model Context Protocol. Enables Claude and other MCP-capable agents to search traces, inspect spans, and analyze service dependencies directly within conversations.
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    MIT
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    🔌 Complete MCP server for Zabbix integration - Connect AI assistants to Zabbix monitoring with 40+ tools for hosts, items, triggers, templates, problems, and more. Features read-only mode and comprehensive API coverage.
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    GPL 3.0
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    24/7 autonomous monitoring and edge detection for prediction markets (Kalshi & Polymarket). Features causal tree analysis, orderbook depth tracking, cross-venue comparison, and real-time alerts.
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    MIT
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    Read-only MCP server for on-prem Linux VMs and PostgreSQL over SSH. Check service health, retrieve bounded logs, inspect DB state, and explore table schemas — without terminal access.
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    MIT
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    An MCP server for the Uptrace observability platform that enables querying traces, spans, logs, and metrics through natural language. It provides tools for error analysis, service discovery, and trace visualization within MCP-compatible clients.
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    Enables AI assistants to interact with and manage Kubernetes clusters, supporting operations on pods, deployments, services, configmaps, secrets, namespaces, metrics, and events with built-in safety features for destructive actions.
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    MIT