RAG Documentation Scraper runs on your own machine — the client starts it, so there is no endpoint to ping. 23 installs a week from npm. Last commit 22 Oct 2026.
Crawl any documentation site and return heading-bounded, LLM-ready chunks with source metadata.
We have not read this server's code yet, 2 h ago · rules 995398eba69f
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
этот файл ставится пользователю, но в репозитории его нет
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add rag-docs-scraper -e 'APIFY_TOKEN=<APIFY_TOKEN>' -- npx -y rag-docs-scraper-mcp-server
{
"mcpServers": {
"rag-docs-scraper": {
"command": "npx",
"args": [
"-y",
"rag-docs-scraper-mcp-server"
],
"env": {
"APIFY_TOKEN": "<APIFY_TOKEN>"
}
}
}
}
[mcp_servers.rag-docs-scraper]
command = "npx"
args = ["-y", "rag-docs-scraper-mcp-server"]
env = { APIFY_TOKEN = "<APIFY_TOKEN>" }
{
"mcpServers": {
"rag-docs-scraper": {
"command": "npx",
"args": [
"-y",
"rag-docs-scraper-mcp-server"
],
"env": {
"APIFY_TOKEN": "<APIFY_TOKEN>"
}
}
}
}
{
"servers": {
"rag-docs-scraper": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"rag-docs-scraper-mcp-server"
],
"env": {
"APIFY_TOKEN": "<APIFY_TOKEN>"
}
}
}
}
Put your own value in place of
<APIFY_TOKEN> (Your Apify API token — https://console.apify.com/account/integrations).
The author’s registry entry marks it as required.
Universal web scraper with LLM-ready markdown, RAG chunking, PDF/DOCX support.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
Extract PDFs to Markdown, RAG chunks and cited tables; publish tracked Doc Links with read stats.
Build a RAG pipeline station by station and search your own documents with cited answers.
Turn any document into a production-ready RAG dataset from Claude, Cursor, or any MCP host.
Search and read Vector Panda docs: API operations, pricing, storage tiers, measured benchmarks.
Free open-source web search and page reading for LLMs, AI agents, and RAG.
Web search, scraping, RAG answers with citations, and translation as MCP tools.
Answers built from our own checks of this server.