Vikunja MCP Server

The Vikunja MCP server bridges the gap between AI assistants and the Vikunja task‑management platform. By exposing a set of well‑defined MCP endpoints, it allows an AI client such as Claude to query and manipulate projects and tasks directly within Vikunja, turning the assistant into a powerful productivity companion.

Solving the Integration Gap

Many developers and teams already use Vikunja to organize work, but interacting with it through a conversational AI often requires manual API calls or custom scripts. The MCP server abstracts these details, presenting the same simple “list projects” or “add task” calls that an AI can invoke naturally. This eliminates the need for developers to write boilerplate code or maintain separate integration layers, saving time and reducing friction when automating routine project‑management workflows.

Core Functionality and Value

At its core, the server offers CRUD operations for projects and tasks. Developers can retrieve a list of all projects, fetch individual project details by ID, and enumerate every task or the tasks within a specific project. Adding a new task is streamlined to only the essential fields—title, description, and completion status—ensuring that the assistant can quickly create items without overcomplicating the payload. Updating and deleting tasks are also supported, giving full control over task lifecycle from within an AI session.

This focused feature set is valuable because it aligns with common use cases: a user can ask the AI to “add a new bug‑fix task to Project X” or “list all pending tasks in the sprint,” and the assistant will translate that into a single API call. The simplicity of the interface means developers can prototype and iterate rapidly, integrating Vikunja into their AI‑driven workflows without deep knowledge of the underlying REST API.

Key Features Explained

• Project Discovery – Quickly enumerate all projects to give the AI context about where tasks belong.
• Task Enumeration – List every task globally or filter by project, enabling the assistant to surface relevant items.
• Task Creation – Create new tasks with minimal required fields, keeping interactions concise and efficient.
• Task Modification & Removal – Update existing tasks or delete them, allowing the AI to manage task states end‑to‑end.

These capabilities are exposed through standard MCP endpoints, so any AI client that understands the protocol can immediately start interacting with Vikunja.

Real‑World Use Cases

• Sprint Planning – An AI assistant can pull all tasks for a sprint, suggest priorities, and add new items based on stakeholder input.
• Daily Stand‑ups – The assistant can report open tasks, mark them as done, or flag overdue items during a meeting.
• Automated Reporting – Generate status summaries or dashboards by querying tasks and projects, then presenting the data conversationally.
• Onboarding Support – New team members can ask the AI to “show me my tasks in Project Y” and receive instant guidance.

These scenarios illustrate how the server turns Vikunja from a static task board into an interactive, AI‑augmented workspace.

Integration with AI Workflows

The MCP server plugs directly into any Claude or similar AI setup via a simple configuration entry. Once registered, the assistant can invoke commands like or . Because the server handles authentication through environment variables ( and ), developers can keep credentials secure while still granting the AI full operational access. This tight integration enables seamless, conversational control over project data without leaving the chat interface.

Unique Advantages

• Minimalism – By focusing on core task‑management operations, the server remains lightweight and easy to maintain.
• Future‑proofing – The design anticipates additional features, allowing developers to extend the MCP with new endpoints as Vikunja evolves.
• Developer‑friendly – The straightforward configuration and clear endpoint names make onboarding quick, even for those new to MCP.

In summary, the Vikunja MCP server transforms a conventional task‑management tool into an AI‑friendly service, empowering developers to build smarter, more responsive productivity assistants that can read, create, update, and delete tasks with natural language commands.