"A media server platform for organizing and streaming content" matching MCP servers:

Enables MCP-compatible agents to manage Tomo Streaming rooms and runtimes, including creating, inspecting, controlling, and deleting rooms, as well as checking health and listing stream 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.

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.

Hosted MCP server (OAuth, no API keys) that makes short drama series (micro-dramas, sitcoms, K-drama style, any language) and other short vertical videos from Claude, ChatGPT or Codex, with the same cast every episode.

Streams real-time log context from Amazon MSK to a Bedrock AI agent for anomaly detection and root cause analysis. Enables AI-powered monitoring with WebSocket push and IAM authorization.

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

Enables AI assistants to inspect, triage, and safely retry media-processing jobs through read/write tools, runbook search, and media probing, with OAuth scopes, rate limiting, idempotency, PII redaction, and human approval before changes.

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

The Problem Modern distributed systems and AI agents require robust operational scaffolding. Simple CRUD apps or mock loops fail when subjected to real-world edge cases, asynchronous boundaries, and security constraints. The Solution An isolated execution platform for AI agents. An agent is given a broken service and a bounded toolset inside a disposable sandbox. Every command passes throug

Enables AI clients to call the documented APIs of real business platforms—job boards, freelance marketplaces, ad networks, e-commerce, messaging, social, sales, trading and market data—through a single CLI and runtime. Serves 455 catalogued platforms with one shared verb vocabulary per category, in both Python and TypeScript.

MCP server for generating and editing images using OpenAI, and creating videos using OpenAI Sora and Google Veo. Enables fetching media from URLs or disk with smart output placement.

A platform-agnostic MCP server that connects Claude to campaign data, institutional knowledge, and historical performance for paid media teams, enabling automated analysis, reporting, and debugging.

Enables control and management of a self-hosted media stack (Radarr, Sonarr, Prowlarr, SABnzbd, qBittorrent) through natural language via MCP.

A production-grade MCP server for downloading audio/videos from the internet, featuring dynamic tool selection, enterprise security, and an integrated graph agent.

MCP server that fetches and converts website pages into clean markdown for agents, with sitemap-based discovery, disk caching, and polite crawling (robots.txt, rate limiting).

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

A self-hosted platform that aggregates upstream MCP servers, OpenAPI imports, and remote device connectors behind a single endpoint per profile, with per-agent scoped authorization, tool/prompt/resource catalogs, and approval-based temporary permissions. It also lets administrators distribute skills and connectors to paired devices and export client configurations.

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.

A shared communication bus for multiple agents using NATS and MCP, enabling MCP clients to list, message, and query agents.

Enables AI clients like Claude and ChatGPT to generate images and videos, animate images, create lip-synced videos, list TTS voices, and manage media via remote MCP tools.

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.

Enables MCP-capable chats and agents to generate images, video, and audio through OpenAI, Google Gemini, fal.ai, and Cloudflare Workers AI, optionally routed via Cloudflare AI Gateway. Supports image editing, asynchronous video jobs, and text-to-speech with per-request provider and model overrides, and can run over stdio or streamable HTTP.

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 AI assistants to interact with the Open Targets Platform API to search and retrieve comprehensive data about target-disease associations, drugs, genes, and drug discovery information through GraphQL queries.

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 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.

Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.