Web Content MCP Server

The Web Content MCP Server solves a common bottleneck in AI‑driven development: accessing and normalizing real‑world information from the web. When an assistant needs up‑to‑date facts, tutorials, or specific articles, it must perform a web search, parse HTML, strip noise, and present the data in a consumable format. This server bundles all those steps into a single MCP‑compatible endpoint, allowing developers to request fresh content without wrestling with HTTP clients, query APIs, or ad‑block logic.

At its core the server exposes two powerful tools. performs a Google query via SerpAPI, returning a list of links along with the extracted main content from each page. pulls a single URL and runs the same extraction pipeline. Both tools output clean Markdown, automatically converting rich HTML into plain text while preserving headings, lists, links, and code blocks. This Markdown is ready for display in chat interfaces, documentation generators, or further NLP processing.

Key capabilities include:

• Intelligent content extraction: Multiple CSS selectors are tried in sequence (, , , etc.) to locate the core article, followed by aggressive cleaning of scripts, styles, navigation bars, and ads.
• Robust error handling: Network failures or malformed pages trigger graceful fallbacks, returning informative messages rather than crashes.
• Type safety: All tool inputs and outputs are validated with Zod schemas, ensuring that clients receive predictable data structures.
• Scalable search: The parameter lets callers request 1–20 results, balancing relevance and bandwidth.

Real‑world use cases abound. A knowledge‑base chatbot can fetch the latest blog post about TypeScript best practices and embed it directly into a user’s conversation. A documentation generator can pull up‑to‑date API references from vendor sites, automatically converting them into Markdown for static site generators. In research workflows, a data‑collection pipeline can query news outlets and store clean summaries in a database for downstream analysis.

Integrating the server into an MCP workflow is straightforward: configure your client’s section to launch the TypeScript entry point via . The server listens on stdio, making it compatible with any MCP‑compliant client—whether built in Rust, Python, or JavaScript. Once connected, the assistant can invoke or as ordinary tool calls, receiving structured Markdown without any additional parsing logic.

What sets this MCP apart is its focus on quality content rather than raw data. By automating extraction, cleaning, and Markdown conversion in one place, it removes a significant friction point for developers building AI assistants that need to stay current with web content.