"Apache Kafka" matching MCP servers:

Enables large language models and AI agents to securely and efficiently interact with Apache Kafka clusters through the Model Context Protocol.

This read-only MCP server lets AI assistants inspect Apache NiFi, ClickHouse, and Kafka across multiple environments through natural language. It provides health checks, flow and message searches, querying and metadata tools while enforcing OIDC or proxy authentication, per-user auditing, secret masking, and two-lock read-only safeguards.

An MCP server that enables interaction with Kafka clusters to manage topics, monitor consumer groups, and stream messages. It provides a comprehensive suite of tools for broker metadata inspection and local Kafka user management.

Ariadne's thread — a way out of the microservice maze. Local cross-service semantic chain hinter for microservices (GraphQL/HTTP/Kafka/frontend)

MCP server that exposes Kafka debugging to an LLM: find messages, measure lag, unblock consumers. Read-only endpoints, previews before writes.

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 lookups across Apache Iceberg Javadocs, local source evidence, and Iceberg REST Catalog operations through MCP tools and prompts.

Read-only MCP server for Kafka cluster health, consumer lag, partition state, and replay-readiness, enabling AI agents to diagnose streaming incidents without write access.

MCP Server for Apache Pinot

A Model Context Protocol server that connects AI agents to Apache Jena, enabling them to execute SPARQL queries and updates against RDF data stored in Jena Fuseki.

Enables AI agents to manage and interact with Apache AGE graph databases through natural language. Supports creating, updating, querying, and visualizing multiple graphs with vertices and edges.

Enables AI-controlled management of Apache Beam data pipelines across different runners (Flink, Spark, Dataflow, Direct) via the Model Context Protocol.

Provides integration with Apache Airflow's REST API, allowing AI assistants to programmatically interact with Airflow workflows, monitor DAG runs, and manage tasks.

An MCP server with 66+ tools for data engineering, enabling AI assistants to interact with Kafka, databases, AWS, data pipelines, infrastructure, and more.

Offline MCP server that checks and scans your own AI prompts and outputs against a local compliance rule corpus. Returns rule ID, severity, citation, and remediation per finding — no API key, Apache 2.0.

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

Open-source mind maps and whiteboard your agent draws on live: 22 tools for mind maps, notes, shapes, tables, charts, connectors, comments and HTML/PDF export; boards are local files.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

Move data between Kafka, NATS, RabbitMQ, MQTT, IBM MQ, SQL databases, MongoDB, files and HTTP: publish messages and run copy routes with endpoints given inline as JSON, while the rows never pass through the model's context.