TaskFlow MCP

TaskFlow MCP is a purpose‑built Model Context Protocol server that turns free‑form user requests into structured, trackable workflows. It addresses the common pain point of AI assistants generating a cascade of tasks that can quickly become unmanageable or drift from user intent. By enforcing a disciplined planning and approval cycle, the server keeps developers and end‑users in control of every step from ideation to completion.

At its core, TaskFlow MCP transforms a single request into a hierarchy of tasks and subtasks. Each node in this tree is annotated with status, dependencies, version tags, and optional notes. The server exposes a rich set of operations—create, update, delete, archive—through MCP endpoints, allowing any AI client to manipulate the workflow without exposing raw data structures. The persistence layer stores tasks in YAML, ensuring that multiline descriptions and complex metadata survive across restarts while remaining human‑readable for audit or manual editing.

Key capabilities include user‑approval gates that pause execution until explicit confirmation, a visual progress table that aggregates status across the entire tree, and export hooks that generate Markdown, JSON, or HTML reports. These features enable seamless integration into CI/CD pipelines, project management dashboards, or conversational assistants that need to surface progress updates. The server also supports relative path resolution for project‑specific task files, making it ideal for multi‑project environments where each repository maintains its own task state.

Real‑world scenarios range from automating content production workflows—where an assistant drafts articles, schedules edits, and tracks publication status—to infrastructure provisioning scripts that break down complex deployments into discrete steps with dependency ordering. In both cases, developers benefit from a single source of truth that can be queried or updated by the AI assistant, ensuring consistency and reducing manual bookkeeping.

What sets TaskFlow MCP apart is its blend of developer‑friendly tooling and robust AI integration. The YAML persistence, coupled with a comprehensive prompts system for consistent LLM guidance, allows teams to tailor the assistant’s behavior without sacrificing traceability. The ability to archive completed requests keeps active lists focused, while the search and restore functions provide a full audit trail. Together, these features make TaskFlow MCP a powerful companion for any AI‑driven project that demands clarity, control, and continuous visibility.