portainer-mcp-connector

name	portainer-mcp-connector
description	MCP connector for Portainer container management – enables AI-powered Docker/Kubernetes operations, stack deployment, and container monitoring through Claude
license	zlib
tags	["cloud","docker","mcp","portainer","containers"]
difficulty	advanced
time_to_master	4-8 weeks
version	1.0.0

Portainer MCP Connector

Overview

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

When to Use This Skill

• Container and stack deployment via natural language
• Docker Compose generation and validation
• Container health monitoring and log analysis
• Image management and vulnerability scanning
• Network and volume management

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Portainer        │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Portainer operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/api/endpoints, /api/stacks, /api/docker/containers, /api/docker/images, /api/docker/networks

Implementation

// Portainer 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: "portainer-mcp-connector",
  version: "1.0.0",
});

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

Example Prompts

"Deploy a WordPress stack with MySQL, persistent volumes, and a reverse proxy configuration"

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

• Portainer 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

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