JDBC MCP Server

What does “JDBC” MCP Server do?

The JDBC MCP Server is a Model Context Protocol (MCP) server designed to act as a bridge between AI assistants and relational databases via the JDBC (Java Database Connectivity) standard. By leveraging this server, developers can empower AI agents to execute database operations, retrieve and manipulate data, and interact with multiple types of SQL databases seamlessly. This capability enhances workflows by enabling tasks such as running queries, performing analytics, and managing data directly through AI-driven interfaces. The JDBC MCP Server simplifies access to disparate databases, making it easier to integrate database-backed functionalities into development and automation pipelines.

List of Prompts

No prompt templates were found or mentioned in the repository.

List of Resources

No explicit resources are detailed in the available documentation or files.

List of Tools

No explicit list of tools could be found in server.py or related files within the repository.

Use Cases of this MCP Server

• Database Query Execution: Enables developers and AI agents to run SQL queries on supported JDBC databases directly from AI-powered tools, streamlining data retrieval and analysis.
• Data Management: Facilitates the creation, updating, and deletion of records in relational databases, which is essential for application development, prototyping, or operational automation.
• Multi-Database Integration: Allows seamless interaction with different SQL database engines (as supported by JDBC), useful for organizations working with heterogeneous database environments.
• Automated Data Reporting: Supports building AI-driven workflows that automatically generate reports by querying databases and formatting the results for end-users.
• Secure Data Access for AI Agents: Provides a controlled interface for AI systems to securely interact with enterprise data sources without exposing direct database credentials.

How to set it up

Windsurf

1. Prerequisites: Ensure Node.js is installed and you have access to the Windsurf configuration file.
2. Locate Configuration: Open your Windsurf configuration file (typically windsurf.config.json).
3. Add MCP Server: Insert the JDBC MCP Server entry in the mcpServers object using the following snippet:

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

4. Save and Restart: Save the configuration and restart Windsurf to load the new MCP server.
5. Verify Setup: Check the logs or Windsurf UI to confirm the JDBC MCP Server is running.

Claude

1. Prerequisites: Install Node.js and access your Claude configuration.
2. Edit Configuration: Open the Claude configuration file (e.g., claude.config.json).
3. Configure MCP: Add the JDBC MCP Server as follows:

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

4. Save and Restart: Save changes and restart Claude.
5. Verify: Confirm via logs or interface that the MCP server is connected.

Cursor

1. Prerequisites: Make sure Node.js is available and locate the Cursor configuration file.
2. Open Config: Edit cursor.config.json.
3. Insert MCP Server:

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

4. Restart Cursor: Apply changes and restart.
5. Check Status: Ensure the server is running via logs or the Cursor dashboard.

Cline

1. Prerequisites: Install Node.js and access the Cline configuration file.
2. Edit Configuration: Open cline.config.json.
3. Add MCP Server:

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

4. Save and Restart: Save and restart Cline.
5. Verify: Confirm availability by reviewing logs or UI.

Securing API Keys

To secure sensitive information like database credentials, use environment variables in your configuration. Example:

{
  "mcpServers": {
    "jdbc-mcp": {
      "command": "npx",
      "args": ["@jdbc/mcp-server@latest"],
      "env": {
        "JDBC_URL": "${JDBC_URL}",
        "JDBC_USER": "${JDBC_USER}",
        "JDBC_PASSWORD": "${JDBC_PASSWORD}"
      },
      "inputs": {
        "jdbc_url": "${JDBC_URL}",
        "jdbc_user": "${JDBC_USER}",
        "jdbc_password": "${JDBC_PASSWORD}"
      }
    }
  }
}

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:

{
  "jdbc-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 “jdbc-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

A solid JDBC MCP implementation with clear setup instructions and security best practices, but missing explicit prompt, resource, and tool definitions. Based on the above, I’d rate this MCP server a 4/10 for documentation and usability.

MCP Score