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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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    51
    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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    An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.
    2
    Apache 2.0
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    Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.
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    MIT
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    An MCP server that gives coding agents programmatic access to Sumo Logic logs via the Search Job API, enabling issue triage in preview deployments and production without leaving the editor.
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    Apache 2.0
  • F
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    Exposes homelab and IT-ops tools to Claude, including system health monitoring, Grafana alert states, Docker container status, Loki logs, SMART disk health, and more.
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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.
    4
    MIT
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    MCP server for Drumbeats monitoring. Enables creating monitors, triaging incidents, and running HTTP/SSL/DNS checks using natural language from any AI client.
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    Apache 2.0
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    Enables LLMs to analyze and manage DBOS workflows, including introspection, workflow management, and authentication, to help debug applications in development or production.
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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 query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.
    1
    MIT