Content Creation MCP Repositories

Fetch and analyze Reddit content, including hot threads and detailed posts with comments from any subreddit. Supports various post types such as text, link, and gallery to provide comprehensive insights.

Transcribes YouTube videos by extracting transcripts, including both manual and autogenerated captions, using the provided video URL. Supports integration with MCP clients for enhanced workflows involving video transcription.

Facilitates the creation of manuals from existing documentation through content analysis, generates metadata, and provides intelligent search capabilities to form a functional knowledge base.

A comprehensive test server that showcases the features of the Model Context Protocol (MCP), providing tools, resources, prompts, and sampling capabilities for developers. It includes implementations in TypeScript and Python for building and testing MCP clients.

Search and access documentation from popular AI libraries such as LangChain and LlamaIndex. Enhance conversations with contextual knowledge by leveraging intelligent documentation extraction.

Enables natural language interactions with personalized Reddit feeds, allowing users to analyze and summarize content relevant to their interests.

Enable natural language interaction with YouTube to fetch trending videos and recent uploads from subscribed channels. Seamlessly access and manage YouTube content without manual API handling.

Enables autonomous visual analysis, testing, and debugging of web interfaces using AI-powered tools. Detect UI bugs, validate user workflows, monitor performance, and generate detailed reports through Playwright integration.

Generate code, write documentation, and answer questions using advanced language models. Utilize the LlamaIndexTS library to enhance coding tasks and improve documentation quality.

Access package documentation from multiple programming languages including Go, Python, NPM, and Rust. It provides structured documentation parsing and language server protocol capabilities for efficient retrieval of information.

Connects to OpenAI's DALL-E 3 model to generate images based on user prompts, saving the results to a specified directory on the user's desktop.

Connects AI coding assistants to DaVinci Resolve for managing video editing projects, timelines, and media through natural language queries. Enables seamless control and interaction within the DaVinci Resolve environment.

Automate browser interactions and perform various testing capabilities, including accessibility checks and visual testing, through a comprehensive MCP server. Provides tools for API testing and debugging, integrating seamlessly with compatible AI systems.

Connects AI models to MIDI-compatible software or hardware by sending MIDI sequences and control messages, while providing virtual MIDI output ports for integration with digital audio workstations and synthesizers.

Facilitates AI-driven content operations and exploration within Sanity projects via natural language interactions. Supports automation of content creation, updates, and semantic searches, while adhering to content schemas and validation rules.

Generates high-quality images from textual prompts with customizable dimensions using the Flux.1 Schnell model. Provides standardized interfaces for specifying image generation parameters and includes error handling features.

Counts the occurrences of a specified letter within a given word, providing accurate letter frequency results for text analysis.

Transcribes audio from YouTube videos to text, providing accurate and fast text representations of video content. Integrates transcription capabilities seamlessly into applications using the MCP framework.

Provides semantic search capabilities over PyTorch documentation to retrieve relevant APIs, code examples, and error messages efficiently. It uses vector embeddings and semantic similarity to enhance search results for developers.

Integrate with the Perplexity API to query programming responses and manage chat history. Maintain conversations and list previous chats using a local data storage system.

Predicts and prevents messenger phishing attacks through AI-based analysis of chat content, providing real-time alerts to users when phishing is detected, aimed at reducing financial loss and personal data breaches.

Provides deep analysis and insights for problem-solving by integrating critical and creative thinking. Offers practical suggestions tailored to the user's context while leveraging existing knowledge and new perspectives.

Provides intelligent integration with WordPress for VS Code by detecting the WordPress environment, connecting to its database, and offering context-aware code completions and documentation support.

Fetch articles and books from Zenn's API and seamlessly integrate them into applications for streamlined development and content access.

Interact with Wikipedia's API to search and retrieve content efficiently, providing access to rich data and insights from various wiki sites.

MCPet is a TypeScript-based server that simulates a virtual pet experience, allowing users to adopt, nurture, and engage with a digital companion that evolves based on user interaction. The pet's stats change over time, reflecting care tasks such as feeding, cleaning, and playing games.

Set up a Next.js blog using Obsidian as the content management system, allowing for easy content management and blog deployment. The project serves as a foundational template for creating and managing a personal blog with minimal configuration.

Retrieve transcripts and subtitles from YouTube videos across multiple languages with enhanced metadata support for content analysis. Provides a simple interface for direct access to video transcripts.

Provides code reviews with a sarcastic tone, identifying issues and suggesting improvements while delivering feedback in a cynical manner. Suitable for developers of all experience levels who appreciate a humorous critique.

Convert web pages into clean, structured Markdown suitable for large language model (LLM) consumption, streamlining the process of feeding web content into AI applications.