Dart MCP Server

The Dart MCP Server is a specialized Model Context Protocol implementation designed to bridge AI assistants with the Dart project‑management platform. By exposing a rich set of tools for task, document, and workspace management, it allows assistants such as Claude to perform complex project operations directly from conversational prompts. The server translates high‑level intent into concrete API calls against Dart, enabling users to create, update, and organize work items without leaving the chat interface.

This MCP solves a common pain point for developers who rely on AI to orchestrate project workflows: the need to switch between a language model and a dedicated task‑management system. With Dart MCP Server, the assistant can pull in real‑time data about tasks, change priorities, assign teammates, and even generate reports—all within the same conversational context. The server’s integration with Dart’s native concepts (spaces, dartboards, folders) ensures that the AI’s actions remain consistent with the user’s existing project structure.

Key capabilities are presented through a straightforward set of tools. For task management, developers can create new tasks, update status or priority, and assign ownership. Document handling is equally robust: markdown content can be authored on the fly, organized into folders, and used to produce instant reports. Workspace management lets users spin up new spaces, configure permissions, and maintain a clean hierarchy of folders—all via simple tool calls. The server also provides helper tools such as and , giving the assistant quick access to configuration data that would otherwise require manual lookup.

Real‑world scenarios include sprint planning, where an assistant can gather all tasks for a given dartboard, reorder them by priority, and assign them to team members—all prompted by natural language. Another use case is documentation automation: a user can ask the assistant to draft a release notes document, which the server creates in Markdown and places into the appropriate folder. In continuous integration pipelines, a Lambda function can invoke these tools to update task status based on build results, keeping the project board in sync with deployment events.

Integration into AI workflows is seamless: developers expose the server to their Claude instance via Smithery or a direct installation, then reference the provided tool names in prompts. The assistant interprets the intent, calls the relevant MCP tool, and returns structured feedback to the user. This tight coupling eliminates context switching, reduces manual errors, and accelerates delivery cycles. The Dart MCP Server’s focus on task orchestration, document generation, and workspace governance makes it a standout choice for teams looking to embed AI directly into their project management lifecycle.