| 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
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",
});
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 }) => {
const response = await fetch(`${BASE_URL}/api/endpoints`, {
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": {
"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
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Portainer 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 Portainer API; sanitize outputs
- 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
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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