"How to create iOS apps" 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.

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.

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

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 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.

An MCP server implementation that enables interaction with the Unstructured API, providing tools to list, create, update, and manage sources, destinations, and workflows.

Connect your Sanity content to AI agents. Create, update, and explore structured content using Claude, Cursor, and VS Code via the Model Context Protocol. Transform content operations from complex queries to simple conversations—giving your team superpowers without sacrificing structure.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables users to create and control portable, user-owned memory archives, including saving memories, reflections, values, and stories, exporting them as open JSON, publishing or discovering public archives, and loading consented material as a clearly-labelled memory-based AI persona.

An integration that allows Large Language Models to interact with Raindrop.io bookmarks through the Model Context Protocol, enabling users to create and search bookmarks directly through AI assistants.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

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

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

Connect any MCP-compatible AI client to the entire FlowDot platform, enabling management of workflows, recipes, custom nodes, apps, knowledge bases, agent toolkits, and community features.

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.

A service that enables agents to build deterministic workflows across apps, databases and APIs using natural language, handling schema mapping, drift detection, and execution of integration pipelines automatically.

An MCP server that connects to SCIM 2.0 APIs, enabling MCP clients to manage SCIM resources using natural language.

Enables AI apps to read Veda Knowledge Packs, offering source-aware context and tools for guided pack interaction.

A lab demonstrating how to deploy a Model Context Protocol (MCP) server and integrate it with Microsoft Copilot Studio, allowing users to connect AI models to different data sources and tools through a standardized protocol.

Enables AI agents to create verifiable, replayable citations, search private knowledge bases, and publish Markdown with verified citation markers.