Apache Kafka is an open-source distributed event streaming platform used for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Provides monitoring and management for Apache Kafka clusters, including cluster/topic metadata, consumer groups and lag, topic creation and configuration, partition management, offset resets, and guarded deletion of topics and consumer groups.
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.
6
64 npm
1
MIT
Why this server?
Provides tools for reading and searching Kafka messages through a Redpanda Console HTTP API, including listing topics, viewing cluster info, describing topic configurations, fetching messages by offset or timestamp, and searching message contents.
Enables AI assistants to read and search Kafka messages through the Redpanda Console HTTP API, including topic listing, message fetching by offset or time, and substring search, without direct Kafka broker access.
7
MIT
Why this server?
Allows AI assistants to interact with Apache Kafka clusters via AKHQ's REST API, providing tools for managing topics, consumer groups, schema registry, nodes/brokers, Kafka Connect, ksqlDB, and ACLs.
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.
58
14 npm
MIT
Why this server?
Connects to EventCatalog instances that document Kafka-based architectures, enabling queries about event schemas, producers, consumers, and message flows
A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.
89 npm
24
MIT
Why this server?
Provides integration with Apache Kafka through Lenses.io, enabling exploration, transformation, and joining of data in Kafka topics across multiple clusters using SQL queries without requiring an additional database.
Manage, explore, transform and join data in Kafka topics across multiple clusters using different flavours of Apache Kafka via Lenses.io (including the free Community Edition)
42
28
Apache 2.0
Why this server?
Provides tools for managing Apache Kafka clusters, including listing and describing topics, consumer groups, cluster metadata, producing and consuming messages, creating and altering topics, and managing ACLs.
Reference implementation of KIP-1318, a secure Model Context Protocol server for Apache Kafka that enables AI agents to interact with Kafka clusters through governed tools and resources.
11
2
Apache 2.0
Why this server?
Provides tools for listing Kafka topics with partition count and replication factor, enabling AI agents to inspect Kafka clusters.
Exposes Kafka administration operations as MCP tools, enabling AI agents to inspect Kafka clusters using natural language.
1
-
Why this server?
Provides publish and consume functionalities for Kafka topics, allowing messages to be sent to and retrieved from Kafka streams. Messages consumed cannot be read again using the same group ID.
Enables AI models to publish and consume messages from Apache Kafka topics through a standardized interface, making it easy to integrate Kafka messaging with LLM and agent applications.
17
Apache 2.0
Why this server?
Supports Kafka as both a streaming source and destination for data pipelines, enabling real-time data ingestion and processing.
MCP server with 32 tools for ETL ingestion, AI-generated data quality rules, AI
transformations, vector search, and natural-language SQL. Works across Postgres,
MongoDB, Kafka, S3/MinIO, HashiCorp Vault, and five vector stores
(Qdrant, Weaviate, Milvus, Chroma, pgvector).
