Dokploy MCP Server

Overview

Dokploy MCP Server bridges the gap between AI assistants and Dokploy’s powerful deployment platform by exposing Dokploy’s API as a set of Model Context Protocol (MCP) tools. Developers can now let Claude, Cursor, or any MCP‑compatible client perform real‑world operations—such as creating projects, managing applications, and querying deployment status—directly from the AI’s conversational context. This eliminates the need for manual API calls or custom scripts, allowing teams to embed deployment logic into chat workflows and automate routine DevOps tasks.

The server focuses exclusively on tool‑centric interactions. Each tool represents a specific Dokploy endpoint (e.g., “Create Project”, “List Applications”) and follows MCP’s standard request‑response contract. When an AI model calls a tool, the server authenticates with the Dokploy instance using an API key and forwards the request, returning structured JSON that the model can parse or pass back to a user. This tight coupling means developers can harness Dokploy’s full feature set—project creation, environment configuration, container scaling—without leaving the AI interface.

Key capabilities include:

• Direct Dokploy API access: Every documented endpoint becomes an MCP tool, enabling fine‑grained control over projects and deployments.
• Secure authentication: The server reads the Dokploy URL and API key from environment variables, ensuring credentials are never exposed in model prompts.
• Extensibility: Because the server is built on a generic MCP framework, new Dokploy endpoints can be added with minimal effort, keeping the toolset up to date as Dokploy evolves.
• Developer‑friendly integration: The server can be launched via , Bun, or Deno and registered in popular MCP clients (Cursor, Windsurf, VS Code), making it accessible across the most common development workflows.

Real‑world scenarios that benefit from Dokploy MCP include:

• Rapid prototyping: A developer can ask an AI assistant to spin up a new Dokploy project and deploy a container image, all from a single conversation.
• Continuous delivery pipelines: CI/CD scripts can invoke MCP tools to trigger deployments or rollbacks without hardcoding API calls.
• Operational monitoring: An AI can query the status of running applications, return metrics, and even trigger scaling actions in response to load changes.
• Collaborative troubleshooting: Team members can request the current configuration of a deployment, receive a detailed response, and discuss fixes in real time.

By embedding Dokploy’s operational capabilities into the AI layer, teams gain a single source of truth for both code and infrastructure. The MCP server turns deployment commands into conversational actions, reducing friction between developers, operations engineers, and the AI assistants that increasingly mediate their workflows.