"How to create a website" matching MCP servers:

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.

Enables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

ake any website queryable by AI agents. Index a site then ask questions and get cited answers grounded in its content via RAG. Two tools: list_sites and ask_site.

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

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

A local RAG MCP server that indexes project documentation and code into ChromaDB for semantic search, supporting multiple tech stacks and deployment modes.

Effortlessly create clean, elegant links in bulk — powered by AI.

Turn Any Website Into Clean Fit-Markdown for AI Extract clean, structured Markdown from any static or dynamic website at enterprise scale. Reduce token costs by 90% and feed pure signal into your RAG pipelines and AI agents.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Enables querying a compressed knowledge graph of the NVIDIA AI developer stack, allowing users to traverse declared dependencies, prerequisites, and relationships across 20 domains via read-only MCP tools like query_ckg, get_prerequisites, and search_concepts.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

A local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.

A Model Context Protocol server for Chroma, enabling AI models to create collections and retrieve data using vector search, full text search, and metadata filtering.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.