Quarkus MCP Server

What does “Quarkus” MCP Server do?

The Quarkus MCP (Model Context Protocol) Server is a collection of servers implemented in Java using the Quarkus MCP server framework. Its main purpose is to extend the capabilities of MCP-enabled large language model (LLM) AI applications by connecting them to external data sources, APIs, or services. By running these servers, developers can enable tasks such as database queries, file management, or integrating with various systems directly from their AI assistants. This enhances development workflows by allowing LLMs to interact with real-world data and services, making it easier to automate, manage, and streamline operations within AI-powered applications. The Quarkus MCP servers are compatible with multiple environments and can be easily integrated into MCP-enabled clients such as Claude Desktop and others.

List of Prompts

No information about prompt templates is provided in the repository.

List of Resources

No explicit resource definitions are provided in the repository documentation.

List of Tools

No direct listing or description of tools in server.py or equivalent files is found in the provided content. However, the JDBC server is mentioned for database interactions.

Use Cases of this MCP Server

• Database Management: The JDBC server allows AI applications to connect to and interact with any JDBC-compatible database (Postgres, MySQL, Oracle, Sqlite, etc.), enabling automated data storage, retrieval, and management through LLM-powered workflows.
• Development Workflow Automation: By providing a bridge between LLMs and various data sources or services, developers can create automated workflows that leverage real-time data or perform operations such as data analysis or transformation.
• Integration with AI Clients: The servers are designed to be used with MCP-enabled clients like Claude Desktop, allowing seamless integration and extended capabilities for AI assistants.
• Cross-language and Platform Support: Since the servers can be run via jbang, they can be used in various environments (Java, JavaScript, Python, etc.), offering flexibility for different development stacks.

How to set it up

Windsurf

1. Ensure you have Java and jbang installed.
2. Open your Windsurf configuration file.
3. Add the Quarkus MCP Server (e.g., JDBC server) to the mcpServers object with a JSON snippet.
4. Save your configuration and restart Windsurf.
5. Verify the server is running and accessible.

Example JSON configuration:

{
  "mcpServers": {
    "quarkus-jdbc": {
      "command": "jbang",
      "args": ["jdbc@quarkiverse/quarkus-mcp-servers"]
    }
  }
}

Securing API Keys:

{
  "mcpServers": {
    "quarkus-jdbc": {
      "command": "jbang",
      "args": ["jdbc@quarkiverse/quarkus-mcp-servers"],
      "env": {
        "JDBC_URL": "your_jdbc_url",
        "JDBC_USER": "${env:DB_USER}",
        "JDBC_PASSWORD": "${env:DB_PASSWORD}"
      },
      "inputs": {}
    }
  }
}

Claude

1. Install Java and jbang.
2. Edit the Claude configuration to add your MCP server.
3. Insert the relevant server details as shown below.
4. Save and restart Claude.
5. Confirm the MCP server is recognized.

Example JSON configuration:

{
  "mcpServers": {
    "quarkus-jdbc": {
      "command": "jbang",
      "args": ["jdbc@quarkiverse/quarkus-mcp-servers"]
    }
  }
}

Cursor

1. Make sure Java and jbang are installed.
2. Open the Cursor configuration file.
3. Add the Quarkus MCP Server in the mcpServers section.
4. Save changes and restart Cursor.
5. Test the integration.

Example JSON configuration:

{
  "mcpServers": {
    "quarkus-jdbc": {
      "command": "jbang",
      "args": ["jdbc@quarkiverse/quarkus-mcp-servers"]
    }
  }
}

Cline

1. Install Java and jbang.
2. Access your Cline configuration file.
3. Add the MCP Server using the JSON format.
4. Save and restart Cline.
5. Ensure the server is operational.

Example JSON configuration:

{
  "mcpServers": {
    "quarkus-jdbc": {
      "command": "jbang",
      "args": ["jdbc@quarkiverse/quarkus-mcp-servers"]
    }
  }
}

Note: For all platforms, secure API keys and sensitive information using environment variables as shown above.

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:

{
  "MCP-name": {
    "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 “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Based on the above coverage, the Quarkus MCP Server repository offers a foundational overview, setup instructions, and security recommendations, but lacks explicit details on prompts, resources, and tools. The documentation is clear on how to run and integrate the servers, especially for database interactions, but is missing more advanced details that would help developers maximize its utility.

MCP Score

Our opinion:
Given the documentation and available features, we would rate this MCP server repository a 6/10. It’s well-structured for basic usage and setup, but more detailed documentation on resources, prompts, and tools would further enhance its usefulness for developers.