Use AI Tools with Confluent Cloud

You can use AI assistants and coding agents to manage Confluent Cloud resources and build streaming applications. Confluent provides MCP servers that connect AI assistants to your environments, clusters, topics, and connectors, and agent skills that guide you through building production-ready Kafka applications.

Manage Confluent Cloud with MCP

The Model Context Protocol (MCP) is an open standard that connects AI assistants to external tools and data sources. Confluent provides managed MCP servers and an open-source MCP server that let AI assistants interact with your Confluent Cloud resources through natural language.

With the MCP servers, your AI assistant can list environments, inspect topics, consume messages, debug connectors, query metrics, write Flink SQL, and more. All actions respect your existing Confluent Cloud permissions. The managed servers provide read-only access, while the open-source server supports both read and write operations. Both servers use your existing credentials and can only access resources those credentials authorize.

Choose an MCP server

Confluent provides managed and open-source MCP servers with different hosting models and capabilities. The following table compares the two options to help you choose.

Feature	Managed MCP servers	Open-source MCP server
Hosting	Hosted by Confluent	Self-hosted on your machine
Transport	Streamable HTTP	stdio, HTTP, SSE
Confluent Cloud support	Yes	Yes
Local Confluent Platform and Kafka support	No	Yes
Kafka topics	Read-only	Read and write
Flink SQL	Not available	Create, list, read, and delete statements
Connectors	Read-only inspection and debugging	Create, read, and delete
Schema Registry	Read-only	List and delete schemas
Tableflow	Not available	Create, read, update, and delete
Metrics	Query metrics	Query metrics
Authentication	Global, Cloud, or Flink API keys	API keys or OAuth
Setup	Configure endpoint URLs in your MCP client	Install with npx and configure a YAML file

For details on setting up each server, see the following pages:

• Access Confluent Cloud with the Managed MCP Servers

• Build with the Open-Source MCP Server

Supported MCP clients

You can use the MCP servers with any MCP-compatible AI assistant, such as the following:

• Claude Code

• Claude Desktop

• Cursor

• VS Code with GitHub Copilot

• Windsurf

• Goose CLI

• Gemini CLI

The managed MCP servers support clients that use streamable HTTP transport. The open-source MCP server supports stdio, HTTP, and SSE transports.

Relationship to the real-time context engine

The MCP servers described on this page help you manage the the following Confluent resources: environments, clusters, topics, connectors, and metrics. The real-time context engine also uses MCP, but serves a different purpose. It delivers real-time business data from your Kafka topics to AI agents and applications.

The two are complementary. You can use the MCP servers to set up and monitor your Confluent Cloud infrastructure, and the real-time context engine to connect your AI applications to the data flowing through that infrastructure.

Build streaming applications with agent skills

Agent skills are AI-powered workflows that guide coding assistants through building production-ready streaming applications step by step. You get code generation, decision-making support, and guidance on Schema Registry integration, security configuration, and error handling.

You can use agent skills to build Kafka producers and consumers, Kafka Streams applications, CDC pipelines, and Schema Registry migration plans. Agent skills work with Claude Code, Cursor, GitHub Copilot, and any AI coding assistant that supports the Agent Skills Specification.

For details, see Build Streaming Applications with Agent Skills.