"Running queries in Apache Superset" matching MCP servers:

Provides AI agents with real-time ground truth about what's actually running on the machine, including live agent sessions, listening TCP ports, loaded daemons, and system stats. It reads directly from the OS via fixed commands, ensuring agents never rely on stale docs or transcripts.

MCP server that lets AI assistants and LLMs interact with Apache Flink clusters through natural language: cluster/job monitoring, metrics, exception tracking, TaskManager resources, and multi-cluster support via the Flink REST API.

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.

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.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

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

Read-only MCP server that exposes Grafana dashboards, datasources, and metric/log queries as tools, for use by skills in the TICKETS workspace.

Enables MCP clients and AI agents to inspect a running FLECS application through its built-in REST API, including querying entities, reading components, explaining queries, and inspecting systems and performance statistics. With explicit opt-in, it also allows small reversible changes to the live ECS world.

Enables AI agents to diagnose broken XAMPP/WAMP localhost stacks by inspecting real machine state — running processes, listening ports, HTTP probes, and Apache/MySQL error logs — to pinpoint port conflicts, startup crashes, unclean-shutdown damage, and zombie processes with plain-English explanations. All diagnostics are read-only, so agents can safely answer questions like why Apache won't start or why localhost refuses to connect instead of guessing.

Enables AI agents to investigate Splunk exports or live queries using deterministic detectors and an iterative analysis loop, all running locally without data leaving the machine.

Headless MCP server for Eclipse MAT that enables analyzing Java heap dumps using built-in commands, OQL queries, and predefined reports.

Enables read-only monitoring and diagnostics of Apache Flink clusters through a JobManager REST endpoint, including job, checkpoint, backpressure, TaskManager, and multi-cluster overviews with LLM-friendly summaries.

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

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

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 LLM clients to ask natural-language questions about nodes in a FABRIC slice, returning grounded answers from the Node Exporter Full dashboard's fixed PromQL queries.

MCP server that lets LLMs interact with a running Prometheus instance via its API, enabling metric analysis, PromQL queries, and rule/target inspection. It also provides embedded documentation and guided runbooks for common Prometheus tasks.

Enables AI assistants to perform automated root-cause analysis of slow Apache Spark and Databricks jobs by listing applications, planning and running diagnoses, retrieving stage and SQL plan details, and listing diagnosis rules via read-only MCP tools.

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

Enables AI assistants to search, manage, and analyze data in Splunk instances through the Model Context Protocol. Supports SPL queries, index management, alerts, dashboards, and more.

Read-only MCP server exposing Flowsery analytics and AI-detected issues via 17 GET tools, enabling natural language queries in compatible clients.

An MCP server for monitoring and managing training jobs on Modal. Built for LLMs that need to check on long-running GPU training without drowning in log output.

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.

Enables AI assistants to execute PromQL queries and discover metrics within AWS Managed Prometheus (AMP) using SigV4 authentication. It provides tools for instant and range queries, label management, and metric discovery in secure, VPC-isolated environments.

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 an AI assistant to diagnose a local WordPress install by listing plugins, checking PHP/WP environment, tailing debug logs, running guarded read-only SQL queries, and inspecting Gravity Forms error and add-on logs.