"Apache HBase" matching MCP servers:

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

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.

Enables AI assistants to manage Apache Ambari clusters, including services, hosts, alerts, and configurations via the MCP protocol.

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

Enables users to interact with Apache Airflow through MCP to monitor DAGs, inspect runs and logs, and diagnose task failures using natural language.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

Exposes Spark History Server data as tools for AI agents, enabling natural language querying of Spark applications, jobs, stages, and performance metrics.

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.

Provides read-only monitoring of Apache Airflow DAGs, including listing active DAGs, fetching recent runs, and analyzing execution history and performance metrics.

Enables users to interact with Apache Airflow orchestration platform through natural language to query pipeline statuses, troubleshoot DAG failures, trigger DAGs, and analyze configurations.

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.

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.

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.

Enables MCP-capable clients to monitor and manage Apache Kafka clusters, including topics, partitions, configs, and consumer groups with lag, while enforcing configurable security modes, topic allowlists, protected topics, and audit logging.

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