Workflowy MCP Server

The Workflowy MCP server turns a user’s private Workflowy account into a fully‑exposed, programmatic API that adheres to the Model Context Protocol (MCP). This solves a common pain point for developers building AI assistants: accessing and manipulating complex, hierarchical note structures without writing custom web‑scrapers or dealing with undocumented endpoints. By authenticating with a username and password, the server bridges the gap between Workflowy’s web interface and AI models that require structured input and output, enabling seamless read‑write interactions.

At its core, the server offers a concise set of tools that mirror Workflowy’s native capabilities. Developers can list_nodes to retrieve root or child items, search_nodes for text‑based queries, and create_node to add new entries. The update_node tool lets an assistant modify existing content, while toggle_complete changes a node’s completion status. These operations are exposed through standard MCP endpoints, so any AI assistant that understands the protocol can invoke them with a simple JSON payload. The result is an AI that can, for example, pull all tasks under a project, automatically mark completed notes after code review, or suggest the next step based on current milestones—all without leaving the assistant’s conversation context.

Real‑world use cases abound. A project manager might let an AI agent scan their Workflowy for pending tasks, synchronize them with a calendar, and send reminders. A developer could have an assistant walk through a codebase stored in Workflowy, flag incomplete sections, and generate documentation snippets. Writers could use the tool to auto‑organize research notes, flag completed drafts, and pull relevant references into a drafting environment. Because the server operates over standard HTTP/JSON, it integrates effortlessly with any MCP‑compatible workflow—whether that’s a custom chatbot in VS Code, a voice assistant, or an enterprise knowledge‑base system.

The Workflowy MCP server’s standout advantages lie in its simplicity and security. Authentication is handled via environment variables, keeping credentials out of codebases. The server’s minimal API surface reduces attack vectors and eases compliance with data‑privacy policies. Moreover, by leveraging MCP’s declarative tool schema, developers can quickly prototype new interactions and extend functionality with minimal overhead. In short, the Workflowy MCP server empowers AI assistants to treat a personal note‑taking system as a first‑class data source, unlocking automation and insight that would otherwise require manual effort.