DeltaTask MCP Server

DeltaTask – A Model Context Protocol‑Enabled Task Management Engine

DeltaTask solves the friction that developers and AI assistants face when trying to orchestrate real‑world tasks within a local, privacy‑preserving environment. Traditional task tools often expose only REST APIs or require cloud services, leaving AI agents without a reliable source of structured task data. DeltaTask flips this paradigm by running as an MCP server on the user’s machine, providing a rich, typed API that AI assistants can query and manipulate without ever leaving the local context. This eliminates latency, respects data sovereignty, and lets developers embed sophisticated task workflows directly into their AI‑powered pipelines.

At its core, DeltaTask is a lightweight SQLite‑backed database that stores every task as an entity with explicit properties: title, description, urgency (1‑5), effort (Fibonacci sequence 1‑21), completion status, tags, and parent/child relationships. The MCP server exposes a set of declarative operations—, , , , and more—that map cleanly onto these data structures. By exposing these operations as MCP endpoints, the server gives an AI assistant a “first‑class” interface to create subtasks, adjust priorities, or fetch statistics in natural language. The result is a seamless integration where an assistant can say, “Schedule the next sprint for me,” and the server will automatically create, prioritize, and sync those tasks.

Key capabilities include:

• Smart Prioritization: Urgency and effort are combined by a built‑in engine that surfaces the most critical work first.
• Task Decomposition: Large items can be split into subtasks with , enabling hierarchical planning.
• Tag‑Based Organization: Custom tags provide flexible categorization, and the server can return all unique tags or filter tasks by them.
• Obsidian Sync: Bidirectional synchronization keeps markdown files and the SQLite database in lockstep, allowing users to edit tasks in their favorite note‑taking environment.
• Analytics: aggregates metrics such as completion rates or effort distribution, giving AI agents insight into workflow health.

Typical use cases span personal productivity and enterprise operations. A developer might integrate DeltaTask with a Claude for Desktop workflow to have the assistant automatically pull new tasks from an email inbox, schedule them into the local vault, and update status after a code review. In a team setting, an AI could monitor a shared Obsidian vault for new project milestones and trigger reminders or status updates via the MCP interface. Because all data remains local, sensitive task information never leaves the user’s machine—an essential advantage for regulated industries or privacy‑conscious teams.

DeltaTask stands out by marrying a robust, locally hosted task engine with the Model Context Protocol’s structured, agent‑friendly API. This combination empowers developers to build AI assistants that can reason about, manipulate, and reflect on real‑world work without compromising data privacy or incurring network overhead.