WeiWanMcp Server

Overview

The WeiWanMcp server is a lightweight, Python‑based MCP implementation that bridges conversational AI assistants with practical web‑scraping and local knowledge‑base workflows. Its core mission is to transform a natural‑language chat interface into an intelligent research assistant that can fetch, format, and organize information from the web without leaving the chat. For developers building AI‑augmented productivity tools, this server eliminates the need to write custom connectors for each source and instead offers a unified toolset that can be invoked directly by the model.

At its heart, the server exposes three primary tools: , , and . The first tool performs targeted search queries against a specified site (e.g., Zhihu) and returns concise links with summaries, enabling rapid discovery of relevant content. The second tool pulls the full article text via a third‑party API and writes it as a Markdown file into a user‑defined notes directory, effectively turning web pages into portable knowledge snippets. The third tool allows the assistant to rewrite or reorganize existing notes, supporting iterative refinement of knowledge artifacts. These capabilities are wrapped in a simple MCP interface that lets an AI assistant like Claude call the tools by name, passing arguments gleaned from user intent.

Developers can embed this server in a broader AI workflow that includes a chat client such as HyperChat. By doing so, the assistant can guide users through end‑to‑end tasks: from asking for the latest MCP discussions, to downloading a chosen article, to summarizing or reformatting it within an Obsidian vault. The server’s design encourages modularity—each tool can be replaced or extended (e.g., adding translation, summarization, or cross‑platform export) without touching the core logic. This makes it a versatile foundation for building custom knowledge‑management pipelines that stay tightly coupled to conversational AI.

Real‑world use cases span research, education, and content creation. A student can ask for recent academic discussions on a topic, download the most relevant papers as Markdown notes, and then have the assistant automatically generate study summaries. A content marketer can pull industry news, auto‑format it into blog drafts, and then push the drafts to a CMS. In enterprise settings, teams can maintain a living knowledge base by letting an AI assistant continuously harvest and curate internal or external resources, ensuring that the most up‑to‑date information is always available in a searchable format.

What sets WeiWanMcp apart is its emphasis on conversational discovery combined with local persistence. Unlike generic web‑scraping scripts, the server is driven by AI intent, so users interact naturally rather than issuing structured commands. At the same time, the Markdown output integrates seamlessly with popular note‑taking ecosystems like Obsidian, enabling powerful search, linking, and versioning. This blend of AI‑powered retrieval and human‑friendly storage gives developers a powerful, plug‑and‑play component for building next‑generation knowledge workflows.