| name | dbeaver-mcp-connector |
| description | MCP connector for DBeaver database tool – enables AI-powered query generation, schema analysis, and database administration through Claude |
| license | Apache 2.0 |
| tags | ["data","database","mcp","dbeaver","sql"] |
| difficulty | intermediate |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
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
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",
});
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 }) => {
const response = await fetch(`${BASE_URL}dbeaver-cli -sql`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
return {
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(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
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Dbeaver API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Dbeaver API; sanitize outputs
- 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
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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