"Allowing a model to access and read content from links" matching MCP servers:

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.

Provides content agents with structured search over editorial guidelines, visual templates, and past post outcomes, enabling auditable proposals that cite rule IDs.

An MCP server that retrieves and provides AI model information from OpenAI, Anthropic, and Google through a unified interface.

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.

Read-only MCP server for Ghost sites that enables AI assistants to search, summarize, and read content via hybrid lexical and semantic ranking.

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 execute any SQL command (including DDL/DML) on a PostgreSQL database with zero restrictions, allowing full database control and dynamic database switching.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Enables agents to query governed metrics through MCP, including describing metrics, running deterministic SQL against a warehouse, explaining join path preferences, and searching semantically ambiguous terms.

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.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables local Vietnamese ecommerce copy preparation, source-grounded RAG retrieval, offline voice previews, correction-memory feedback, hook review, and fine-tuning dataset preparation without publishing posts or approving audio.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

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.

Read-only MCP server for a content contract layer: protocol, modules, pipelines, assets, library. 一句话 / One-liner（ZH）：给任何 AI agent 一个可校验的内容规范与资产底座——先查契约，再落笔。 描述 / Description：只读 MCP 服务器：把长内容的验收标准（协议 / 模块 / 管线 / 资产 / 馆藏）变成 agent 可调用的检索与取件工具面。10 个只读工具 + 1 个 prompt（assemble_guide）；支持 modern 2026-07-28 与 legacy 2025-11-25 双协议；零第三方依赖（Python 标准库）；无写路径。 仓库 / Repo：https://github.com/Monyeah777/NinFenz 站点

Search reusable technical experience from AI agents, inspect evidence and limitations, and discover public agent Trust Passports through seven anonymous read-only MCP tools. Apache-2.0 integrations, examples and documentation connect to the separately governed hosted Remnant service. Agent OAuth and Candy are separate surfaces.

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.

An MCP server that provides read-only access to MySQL databases.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

A server that leverages Cloudflare Browser Rendering to extract and process web content for use as context in LLMs, offering tools for fetching pages, searching documentation, extracting structured content, and summarizing content.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Stores Organizations content

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