"Information about the website ritwikjoshi.com" matching MCP servers:

Enables AI clients to search curated, cited knowledge bases built from founders', CEOs', and investors' public talks, essays, and posts, returning passages with source links and dates. It also exposes available source collections via MCP tools so users can ask how those individuals might approach business, product, or growth questions.

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.

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 AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

This server enables users to download entire websites and their assets for offline access, supporting configurable depth and concurrency settings.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables AI agents to scrape, crawl, clean, chunk, and index website content into Azure AI Search for full-text retrieval.

A production-grade AI chatbot that enables real-time information retrieval and retrieval-augmented generation for website content. It integrates MCP tools to fetch live data while providing a responsive chat interface with strict guardrails against misinformation.

Provides tools to fetch and search news from The Verge's RSS feed, allowing users to get today's news, retrieve random articles from the past week, and search for specific keywords in recent Verge content.

Enables retrieval-augmented question answering over a private podcast archive with hybrid search (BM25 + dense embeddings), returning ranked passages with timestamps and citations.

Exposes publisher journalism to LLMs and agent frameworks via search, retrieval, source grounding, and consistent citation, enabling accurate content retrieval with attribution and policy enforcement.

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

Provides access to 61 public digital libraries through a single unified interface, enabling users to search and retrieve information from academic papers, books, legal records, and more using natural language.

Enables LLM-based agents to interact with FHIR healthcare data through natural language prompts, providing full CRUD operations on FHIR resources, document processing, and semantic search capabilities.

Enables AI and LLM systems to interact with the Buttondown newsletter service, supporting email drafting, scheduling, analytics retrieval, and list management through a Model Context Protocol interface.