Dbeaver MCP Connector

Dbeaver MCP Connector

Overview

This skill enables Claude to interact with Dbeaver through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Dbeaver's CLI/Plugin, allowing natural language control of Dbeaver operations, intelligent automation, and AI-powered assistance for Dbeaver workflows.

When to Use This Skill

• SQL query generation from natural language
• Schema visualization and relationship analysis
• Data export and transformation planning
• Database migration script generation
• Performance analysis and query optimization

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Dbeaver          │
│   (Client)   │◀────│   (Java/TypeScript)  │◀────│   (CLI/Plugin    )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Dbeaver operations as tools Claude can invoke. The server translates natural language intentions into CLI/Plugin calls.

Key Endpoints/Interfaces

dbeaver-cli -sql, dbeaver-cli -connect, dbeaver-cli -export, ERDiagram API

Implementation

// Dbeaver MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "dbeaver-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Dbeaver resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Dbeaver CLI/Plugin
    const response = await fetch(`${BASE_URL}dbeaver-cli -sql`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Dbeaver",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}dbeaver-cli -sql`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Dbeaver data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}dbeaver-cli -sql`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "dbeaver-mcp-connector": {
      "command": "node",
      "args": ["path/to/dbeaver-mcp-connector/index.js"],
      "env": {
        "DBEAVER_API_KEY": "your-api-key",
        "DBEAVER_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Dbeaver API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Dbeaver API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Generate an SQL query to find the top 10 customers by revenue in the last quarter with product breakdown"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Dbeaver Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.