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Webgate

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Developer Tools
Data Science & ML
Web Scraping & Data Collection

Delivers clean, bounded web content to AI clients, preventing context window flooding during web searches and URL fetches.

mcp-webgate acts as an intelligent intermediary between your AI and the web, addressing the critical problem of raw HTML overwhelming AI context windows. When AIs use standard fetch tools, they receive cluttered web pages filled with ads, scripts, and menus, potentially consuming hundreds of thousands of tokens and derailing ongoing conversations. Webgate surgically strips this irrelevant noise, hard-caps every response to a defined character budget, and can optionally summarize multiple sources into a concise, cited Markdown report using a secondary LLM. This ensures your AI always receives relevant, structured, and manageable web content, significantly enhancing its ability to perform effective research and retrieval across various clients like IDEs and CLI agents.

Key Features

01Utilizes BM25 and optional LLM-assisted reranking for enhanced search result relevance.
023 GitHub stars
03Surgical HTML parsing to strip ads, scripts, and navigation elements from web pages.
04Optional LLM summarization of multiple fetched sources into a concise, cited Markdown report.
05Supports integration with various search backends including SearXNG, Brave, Tavily, Exa, and SerpAPI.
06Hard-caps total response size and per-page limits to prevent AI context window flooding.

Use Cases

01Enhancing web search capabilities for AI models integrated within IDEs like Claude Desktop, Zed, and VSCode.
02Providing clean and bounded web content to CLI-based AI agents, such as Gemini CLI and Claude CLI.
03Conducting deep research on complex topics with LLMs without exceeding context window limitations.