"How to Download Videos" 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.

Lets AI agents search and discover skill files from a curated catalog to fill missing capabilities, providing download URLs for free items and purchase info for paid ones.

Enables Codex to delegate coding tasks to an OpenCode CLI locally, returning structured results such as exit codes, session summaries, tool calls, and git diffs.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Enables routing tasks to the right specialist agents, assembling the needed context from a layered catalog of agents, skills, knowledge, runbooks, topics, tools, and connectors, while remembering how a business wants work done.

Teach any AI agent how you use an MCP server – Soft-Landing to your AI integrations

Enables learning how to build and use an MCP server with tools, resources, and prompts through a Python example.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

Enables AI agents to analyze videos and images using Qwen3-VL deployed on Modal, supporting hours-long videos with timestamp grounding, text extraction, video summarization, and Q\&A with 256K context window.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.

An MCP server that connects to Pollinations.ai API, enabling AI models to generate and download images and text through natural language commands.

A Model Context Protocol server that converts Markdown content to HTML format.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server integrating with various LLM clients.

Enables two LLMs to play Tic-Tac-Toe against each other autonomously using a shared tool and an SSE relay. The server facilitates agent-to-agent communication by holding tool responses until the opponent makes a move, managing the game state in real-time.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables users to ask questions to a persona named Vincent and get yes/no answers or personalized responses in Vincent's style. Provides a tool to simulate how Vincent would respond to various statements or questions.

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

Enables AI agents to coordinate atomic distributed transactions across a swarm through a prepare/vote phase, unanimous commit-or-abort consensus, quorum validation, and failure recovery. It also layers in vector-clock causal stamping, LWW CRDT reconciliation, and gossip dissemination so multi-node state converges deterministically without external dependencies.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

Enables any agent to spawn and manage T3 Code agent threads over HTTP MCP — starting research or coding agents, sending follow-ups, waiting for results, and answering approval prompts. It pairs once as a T3 device, exposes 15 tools, and keeps operations retry-safe.

Industrial-grade MCP server for Siemens TIA Portal (V17–V21). 120+ tools for PLC programming: create blocks (FB/FC/OB/DB), manage tags, compile, download, and simulate with PLCSim Advanced. On-premise, sovereign-AI compatible.

Exposes your organization's Terraform style guide to AI coding agents, enabling them to generate, validate, and ensure Infrastructure as Code follows your specific conventions, naming standards, security defaults, and best practices.

Enables defenders to deploy a decoy MCP tool server that records and fingerprints how LLM agents probe, escalate, and persist, without exposing real systems.

A video analysis system that uses AI vision models to process, analyze, and query video content through natural language, enabling users to search videos by time, location, and content.

Enables ChatGPT to securely control an existing Microsoft Edge session on Windows via OAuth-protected HTTPS, including tab management, navigation, page interaction, and file upload/download through an MCP proxy.

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.