by portainer · MCP Server · ★ 191
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Portainer MCP Official MCP server for Portainer, generated from the Portainer OpenAPI spec via FastMCP. Overview This MCP server exposes the Portainer REST API as MCP tools: list and inspect environments, manage GitOps workflows, troubleshoot Docker and Kubernetes resources. It also supports proxying requests to the underlying Docker and K8s APIs of each environment. Match the MCP server's minor version to your Portainer instance's minor — e.g. MCP server 2.42.x with Portainer 2.42.x. See Version compatibility for details. Getting started The MCP server can be executed locally via or as a container. Use the first approach to explore the MCP capabilities locally and deploy it inside your infrastructure as a container for a team based deployment setup. [!NOTE] Before using the MCP, make sure to generate an API key in Portainer under My Account → Access tokens first as both paths need it. Single user (stdio via ) The recommended way to test the MCP server locally. Runs as a stdio process on your machine and connects directly to the Portainer instance. [!NOTE] must be installed and available on . See the uv install docs.
| Stars | 191 |
| Forks | 42 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.7989448366317/100 |
| Open Issues | 3 |
| Last Updated | 2026-07-02 |
| Created | 2025-03-09 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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portainer-mcp is Official Portainer MCP server. It is categorized as a MCP Server with 191 GitHub stars.
portainer-mcp is primarily written in Python. It covers topics such as ai, containerization, llm.
You can find installation instructions and usage details in the portainer-mcp GitHub repository at github.com/portainer/portainer-mcp. The project has 191 stars and 42 forks, indicating an active community.
portainer-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to portainer-mcp on Agent Skills Hub include agentic-radar, hyper-mcp, flow-like. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.