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    Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2
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    MIT
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    A unified local API gateway providing caching, rate limiting, and full Model Context Protocol compatibility for AI agent integration. It enables users to aggregate multiple API endpoints into a single gateway with built-in observability and customizable eviction strategies.
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    Monitor the real-time status of 200+ popular APIs and services. Check if services like GitHub, Stripe, AWS, and Slack are experiencing outages or degraded performance directly from your AI assistant.
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    MIT
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    Provides secure access to the LogicMonitor API, enabling AI assistants to manage monitoring infrastructure through natural language commands.
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    Mozilla Public 2.0
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    A secure MCP proxy server with JWT authentication, SQL guardrails, rate limiting, and Kafka audit logging, enabling enterprise-grade access control for MCP clients like Cursor and Claude Desktop.
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    An instrumented MCP server for debugging lost tool calls in Claude Desktop, using ping, echo, sized_echo, and slow_echo tools to record and analyze the full protocol flow.
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    MIT
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    Read-only MCP server for Umami analytics. It talks to the Umami REST API directly over HTTP, supporting self-hosted and cloud setups.
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    MIT
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    An MCP server for Microsoft Defender Advanced Hunting that enables AI assistants to investigate security events using natural language by translating queries to KQL and executing them against Defender.
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    MIT
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    Enables MCP-compatible coding agents to debug applications using runtime log data, by providing tools to start a local log-ingestion server, track debugging sessions and hypotheses, and correlate logs to specific executions.
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    MIT
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    Enables AI agents to access New Relic logs and APM data through the NerdGraph API. It allows users to execute NRQL queries, retrieve application performance metrics, and analyze transaction traces using natural language.
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    Enables LLMs to analyze Linux perf data files using 26 perf analysis commands, including report, script, annotate, and more, through typed tool parameters.
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