"Servers for hosting a website with a modern tech stack" matching MCP servers:

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables diagnosing web hosting and server issues by running live HTTP, SSL, DNS, and security-header checks alongside a searchable troubleshooting knowledge base.

Diagnoses web hosting and server issues by combining live HTTP, SSL, DNS, and security-header checks with a searchable knowledge base of troubleshooting notes, enabling MCP clients to investigate symptoms and explain root causes.

Enables Codex agents to save, retrieve, and manage technology bookmarks as sourced, reviewable cards, with consent-based suggestions and trial planning.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A MCP server that gives Claude Code and other AI assistants long-term memory by automatically extracting technical knowledge from conversations and retrieving relevant experiences in future sessions.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Connects to Stack AI workflows, allowing you to run workflows and pass user inputs through MCP-compatible clients.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

Enables AI agents to fetch webpages locally, extract main content, and convert it into clean, token-efficient Markdown with preserved links. Supports configurable multi-page crawling, caching, robots.txt compliance, and optional chunking for downstream processing.

Fetches web pages, strips noise, and converts them to clean Markdown with preserved links and optional multi-page crawling, enabling token-efficient web content extraction for AI agents and LLM pipelines.

Enables AI agents and LLM pipelines to fetch and crawl webpages, strip extraneous content, and convert them into clean, link-preserving Markdown for fast, token-efficient scraping.

Fetches webpages—and optionally crawls linked pages up to a configurable depth—then strips noise and converts the content into clean, token-efficient Markdown with links preserved. It caches results, respects robots.txt, and streams content for low memory use, making it suited for feeding AI agents, IDEs, and LLM pipelines readable web content.

Provides a local groundedness verifier as an MCP server, returning a verdict on whether a draft answer is supported by provided document excerpts.

Enables users to connect and run Stack AI workflows directly through MCP-compatible clients. It allows for passing user inputs to specific projects and receiving structured responses from deployed AI applications.

Enables AI agents and LLM pipelines to fetch webpages or crawl sites locally and convert the content into clean, token-efficient Markdown with links preserved. Includes caching, robots.txt-aware rate limiting, concurrent depth crawling, and optional chunking for downstream processing.

Enables AI agents to fetch web pages locally, strip noise, and convert content to clean Markdown while preserving links, with caching and optional multi-page crawling for token-efficient scraping.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Aggregates technical articles from multiple sources (Qiita, Dev.to, NewsAPI, Hacker News), summarizes them using Gemini API, and syncs them to Notion databases with personalized recommendations based on reading history and tags.