Confluent MCP Server Integration

What does “Confluent” MCP Server do?

The Confluent MCP Server is an implementation of the Model Context Protocol (MCP) that empowers AI assistants to interact seamlessly with Confluent Cloud REST APIs. By integrating this server, AI tools such as Claude Desktop and Goose CLI can manage Kafka topics, connectors, and Flink SQL statements using natural language. This enhances development workflows by allowing AI-driven automation and orchestration of streaming data infrastructure. The server bridges AI agents and complex data systems, streamlining tasks like topic management, connector operations, and SQL job handling, and making it easier for developers to leverage Confluent’s capabilities programmatically.

List of Prompts

No prompt templates are mentioned in the provided repository content.

List of Resources

No explicit resources are described in the provided repository content or README.

List of Tools

No explicit tool list is provided in the README or main documentation. The server enables management of Kafka topics, connectors, and Flink SQL statements, but specific tool definitions are not listed.

Use Cases of this MCP Server

• Kafka Topic Management
  Allows developers to create, update, and manage Kafka topics in Confluent Cloud through natural language, streamlining data pipeline setup.
• Connector Orchestration
  Enables AI assistants to manage and configure Confluent connectors for integrating external systems, reducing manual configuration steps.
• Flink SQL Job Management
  Facilitates the submission, monitoring, and management of Flink SQL statements, simplifying real-time stream processing tasks.
• Automated DevOps for Streaming Data
  Provides command and control over streaming infrastructure, supporting automated operations and maintenance through conversational interfaces.
• Integration with AI Tools
  Seamlessly connects with tools like Claude Desktop and Goose CLI, giving developers a powerful interface to interact with Confluent Cloud via AI agents.

How to set it up

Windsurf

1. Ensure you have Node.js installed.
2. Locate your Windsurf configuration file.
3. Add the Confluent MCP server using the syntax below.
4. Save your configuration and restart Windsurf.
5. Verify the server connection in the Windsurf UI.

"mcpServers": {
  "confluent-mcp": {
    "command": "npx",
    "args": ["@confluentinc/mcp-confluent@latest"]
  }
}

Claude

1. Make sure Node.js is installed on your system.
2. Open your Claude Desktop configuration file (see example.claude_desktop_config.json in the repo).
3. Insert the following snippet under mcpServers.
4. Save the file and restart Claude Desktop.
5. Confirm MCP connection in Claude.

"mcpServers": {
  "confluent-mcp": {
    "command": "npx",
    "args": ["@confluentinc/mcp-confluent@latest"]
  }
}

Cursor

1. Install Node.js if it’s not already present.
2. Edit the Cursor configuration file.
3. Add the Confluent MCP server configuration.
4. Save the file and restart Cursor.
5. Test the server connection.

"mcpServers": {
  "confluent-mcp": {
    "command": "npx",
    "args": ["@confluentinc/mcp-confluent@latest"]
  }
}

Cline

1. Confirm Node.js is available on your system.
2. Find and open the Cline configuration file.
3. Add the server configuration as shown below.
4. Save and restart Cline.
5. Check for successful server registration.

"mcpServers": {
  "confluent-mcp": {
    "command": "npx",
    "args": ["@confluentinc/mcp-confluent@latest"]
  }
}

Securing API Keys

Use environment variables for sensitive information. Here’s how you can specify them in your configuration:

"mcpServers": {
  "confluent-mcp": {
    "command": "npx",
    "args": ["@confluentinc/mcp-confluent@latest"],
    "env": {
      "CONFLUENT_API_KEY": "${CONFLUENT_API_KEY}",
      "CONFLUENT_API_SECRET": "${CONFLUENT_API_SECRET}"
    },
    "inputs": {
      "apiKey": "${CONFLUENT_API_KEY}",
      "apiSecret": "${CONFLUENT_API_SECRET}"
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "confluent-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “confluent-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Roots support: Not specified
Sampling support: Not specified

Based on the available documentation, the Confluent MCP Server provides basic integration details and clear setup instructions for major MCP-supported platforms, but lacks depth in prompt, resource, and tool documentation. The README highlights the main use cases but omits technical specifics about resource and tool primitives.

My rating: 4/10.
The project provides essential integration info and demonstrates utility, but lacks comprehensive MCP documentation (tools/resources/prompts), which limits its immediate usability for advanced or customized workflows.

MCP Score