"Apache Lucene" matching MCP servers:

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.

An MCP server for querying and analyzing Apache Incubator podling health reports. It provides tools to list, search, and compare podling metrics across different time windows from Apache's health report Markdown files.

Enables querying OpenTelemetry logs stored in OpenSearch across development and production environments. Provides tools for searching logs by various criteria including free-text Lucene queries, trace IDs, service names, error levels, and specific fields.

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

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

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 wraps the Apache Airflow REST API, enabling clients to manage DAGs, monitor task instances, and handle workflows through a standardized interface. It provides comprehensive access to Airflow features including DAG runs, variables, connections, and XComs.

Enables AI-assisted management of Apache Tomcat, including start/stop, WAR deployment, log analysis, thread and heap dumps, and diagnostics.

An MCP server that enables querying logs and metrics from Graylog, Prometheus, and InfluxDB 2.x. It provides tools for executing Lucene log searches, PromQL queries, and Flux queries directly within MCP-compatible clients.

Enables AI assistants to search and analyze logs in Graylog using three powerful tools: generic log search with Lucene queries, smart UUID/trace ID lookup across multiple fields, and stream-specific message retrieval with automatic field normalization.

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 DAG management, monitoring, debugging, and connection testing for Apache Airflow through the MCP protocol.

Deploys 25 MCP tools for Apache Doris, enabling SQL execution, metadata queries, monitoring, and data governance over HTTPS via AWS Bedrock AgentCore Runtime.

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.

A Model Context Protocol (MCP) server that interfaces with the Apache YuniKorn Scheduler, allowing AI agents to observe and reason about Kubernetes batch workload resource management, queue hierarchies, and application states.

An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.

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 dependency-free MCP server for Apache Incubator PMC oversight that helps identify podlings needing attention, assess graduation readiness, and generate podling briefings by combining lifecycle data and community health signals.