---

name: openvas-mcp-connector description: "MCP connector for OpenVAS vulnerability scanner – enables AI-powered vulnerability scanning, report analysis, and remediation planning through Claude" license: GPL tags: ["security","vulnerability","mcp","openvas","scanning"] difficulty: advanced time_to_master: "4-8 weeks" version: "1.0.0"

Openvas MCP Connector

Overview

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

When to Use This Skill

• Scan target and task configuration via AI
• Vulnerability report analysis and prioritization
• Remediation plan generation from scan results
• Scan scheduling and policy management
• Compliance audit configuration

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Openvas          │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (GMP (Greenbone)│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Openvas operations as tools Claude can invoke. The server translates natural language intentions into GMP (Greenbone Management Protocol) calls.

Key Endpoints/Interfaces

create_target, create_task, start_task, get_results, get_reports

Implementation

// Openvas 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: "openvas-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Openvas 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 Openvas GMP (Greenbone Management Protocol)
    const response = await fetch(`${BASE_URL}create_target`, {
      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 Openvas",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}create_target`, {
      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 Openvas 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}create_target`, {
      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": {
    "openvas-mcp-connector": {
      "command": "node",
      "args": ["path/to/openvas-mcp-connector/index.js"],
      "env": {
        "OPENVAS_API_KEY": "your-api-key",
        "OPENVAS_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 Openvas 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 Openvas API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Run a full vulnerability scan on the production subnet and generate a prioritized remediation report"

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

• Openvas 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

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

---

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