"A method for retrieving GitHub README file content" matching MCP servers:

Extract content from URLs, documents, videos, and audio files using intelligent auto-engine selection. Supports web pages, PDFs, Word docs, YouTube transcripts, and more with structured JSON responses.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Lets AI assistants manage a self-hosted content hub: create, edit, publish, and update articles across nine platforms, refresh account article lists, and sync changes through natural language.

Enables AI agents to analyze a piece of content across 11 dimensions and receive ranked, scored recommendations among six cross-media output formats (PPT, infographic, comic, short video script, newsletter, reusable skill), then generate dispatch prompts for the chosen format. It also exposes the format catalog and profile schema as read-only tools, supporting batch routing of an entire content library.

Multi-platform content distribution — draft posts, repurpose content, generate carousels, schedule, analyze performance, create threads. 7 MCP tools.

MCP server for publishing posts and retrieving metrics on social platforms like Telegram, with planned support for YouTube and Instagram.

A powerful MCP tool for parsing and manipulating MIDI files that allows users to read, analyze, and modify MIDI files through natural language commands, supporting operations like reading file information, modifying tracks, adding notes, and setting tempo.

MCP server that fetches raw HTML content from a given URL to provide web context to LLMs.

A specialized Model Context Protocol server that enhances AI-assisted medical learning by connecting Claude Desktop to PubMed, NCBI Bookshelf, and user documents for searching, retrieving, and analyzing medical education content.

Enables comprehensive GitHub workflow automation including Actions monitoring, PR management, code search, file operations, and repository management through a code-first architecture that reduces token usage by 98%.

A secure file server for AI assistants that provides comprehensive file operations and text manipulation with configurable access levels and multiple connection modes.

Provides SEO audits, content analysis, and domain overviews through an MCP server, including on-page, technical, and authority metrics with built-in browser rendering and anti-bot handling.

MCP server exposing content contract validators (SEO.md, PRODUCTHUNT.md, LINKEDIN.md) as tools so AI agents can verify generated content against concrete rules without maintaining linters. It provides linting and JSON-LD export tools with config and content passed as parameters, not file paths.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Enables reading and verifying C2PA Content Credentials from images and videos through both embedded manifests and invisible watermarks. Supports direct filesystem access, URL checking, and provides structured provenance data including creator identity, AI generation info, and content authenticity.

Enables saving and managing files in a sandboxed local folder via MCP, with tools for listing, reading, writing, appending, searching, moving, and deleting files and directories.

Enables AI agents to extract clean, structured web content (articles, tables, links, visual layouts) optimized for LLM token efficiency, with fast response times and optional JavaScript support.

Enables AI clients to browse instruments, items, dimensions, expert assignments, and calculate Content Validity Index (CVI) via a standard MCP interface.

An MCP server that uses AI to generate structured Asana project plans from diverse sources like text, PDFs, and meeting transcripts. It enables users to preview, generate, and directly sync detailed task lists, subtasks, and dependencies into Asana.

Enables AI-driven batch refactoring of Movable Type content via the MT Data API using natural language instructions. It provides a secure workflow featuring automated backups, visual diff reports, and rollback capabilities for safe HTML transformations.