"Apache Kafka" matching MCP servers:

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.

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

A comprehensive Model Context Protocol (MCP) server for Grafana, Prometheus, Kafka UI, and Datadog. Features a secure "Bring Your Own Key" (BYOK) architecture where credentials stay local. Provides tools for metrics querying, dashboard inspection, Kafka lag monitoring, and unified health checks.

Provides intelligent command completion, validation, and contextual help for the Kafka Docker Playground CLI. Integrates with GitHub Copilot to offer auto-complete suggestions, detailed command assistance, and debugging support for playground operations.

Connects LLM clients to Apache Superset for querying charts, dashboards, datasets, databases, and executing SQL.

An MCP server implementation built to interact with Confluent Kafka and Confluent Cloud REST APIs.

Enables AI assistants to interact with Apache Kafka clusters through AKHQ's REST API. Supports multi-environment configuration, flexible authentication, and comprehensive coverage of topics, consumer groups, schema registry, Kafka Connect, ksqlDB, and ACLs.

A proof of concept MCP server for Apache OFBiz

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.

A Model Context Protocol server enabling Claude to maintain user context through Apache Unomi profile management.

The accountability layer for AI agents — a named human's signed yes before an agent does anything irreversible (payment, record change, deploy), then an offline-verifiable Trust Receipt. Apache-2.0, formally verified.

Sovereign, encrypted, persistent memory for AI agents. Eight MCP tools — remember, recall, forget (GDPR Art. 17 erasure), share, governance. Anchored on COTI V2 mainnet. Apache-2.0.

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.

An MCP server that converts Apache Airflow DAGs into Prefect flows. It provides tools to read DAGs, lookup translation knowledge, validate code, search Prefect docs, scaffold projects, deploy, and generate migration reports.

Automates the full setup and deployment of Ballerina + Apache ActiveMQ integrations inside a WSO2 Integrator workspace, from broker validation to a runnable BI-low-code-ready project.

Agent-native hosting: your agent deploys any folder to a live URL in one command — no account, no dashboard, no CAPTCHA — and hands the human a link plus a plain-language summary. Full deploy lifecycle as MCP tools (deploy, verify, logs, inspect, rollback), production approval human-only; static sites and full Node.js apps, open source (Apache-2.0).

A Model Context Protocol server that renders Apache ECharts options to SVG, enabling AI agents to generate deterministic charts server-side without a browser.

Carbon-aware MCP scheduler that defers "do it later" / "by tomorrow" / "overnight" LLM tasks to the cleanest electricity-grid hour inside a deadline — 40-70% lower CO2 per task, 50% cheaper via Anthropic/OpenAI Batch APIs. 9 MCP tools, per-task carbon receipts to local SQLite, Apache-2.0.

MCP server for AWS infrastructure analysis. Connects to DynamoDB, Lambda, SQS, SNS, Secrets Manager, EventBridge, RDS, PostgreSQL, MongoDB, and CloudWatch — surfaces missing indexes, queue DLQs, Lambda config issues, IaC drift, and security findings for Claude Code and Cursor.

Gives language models a working connection to Apache Jena Fuseki: SPARQL queries and updates, whole named-graph reads and writes over the Graph Store Protocol, bulk RDF loading from a sandboxed directory, and dataset administration such as listing, creating, backing up and compacting datasets. Named graphs are also exposed as MCP resources, and a read-only mode hides the seven writing tools.