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    Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.
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    Apache 2.0
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    MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.
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    Apache 2.0
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    Enables AI assistants to manage Apache Ambari clusters, including services, hosts, alerts, and configurations via the MCP protocol.
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    Apache 2.0
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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 toolkit of 256 MCP servers for SRE incident diagnosis with Claude. One agent per tech (Postgres, Kafka, Istio, Kubernetes, Prometheus, MongoDB, Redis, Cassandra, ...) with failure modes, key metrics, and runbooks baked in. Plus telemetry MCPs (PromQL/LogQL/Elasticsearch), SSH-via-bastion executor, and 33 discovery adapters across 9 clouds. Apache 2.0, runs locally. Reproducible 5/5 scenari
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    Apache 2.0
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    An MCP server that gives AI agents real-time observability into Apache Kafka clusters, enabling natural language queries for broker health, consumer lag, and diagnostics.
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    MIT
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    Enables users to interact with Apache Airflow orchestration platform through natural language to query pipeline statuses, troubleshoot DAG failures, trigger DAGs, and analyze configurations.
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    10
    MIT
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    Enables comprehensive analysis of Apache Spark event logs from S3, HTTP, or local sources, providing performance metrics, resource monitoring, shuffle analysis, and automated optimization recommendations with interactive HTML reports.
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    MIT
  • F
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    Enables AI assistants to manage and monitor Apache Kafka clusters through natural language, providing real-time operations, health monitoring, consumer lag analysis, and temporal trend detection for intelligent cluster management.
    Last updated