"Information about Open Source Intelligence (OSINT)" matching MCP servers:

Real-time video intelligence

Enables small models to turn a short brief into complete HTML pages or looping GIFs with typography, palette, borders, motion, and validation already decided. It also lets agents list styles, retrieve style rules, create assets, validate HTML, and access reference examples.

Provides image recognition capabilities to Codex/Claude Code by routing 'look at screen/screenshot' requests to a multimodal model via an API relay. Enables UI automation agents to analyze local screenshots and return descriptions or structured JSON coordinates.

Enables AI clients like Codex, Claude Code, and Kimi Code to analyze public video URLs by downloading media, uploading it to Gemini, and returning timestamped production breakdowns covering shots, visual design, animation, motion, narration, music, sound effects, and editing, with follow-up Q&A and session management tools.

Provider-neutral Vision MCP Server — image analysis via 10 inference providers through a single MCP server.

Enables AI coding agents to perform end-to-end medical image analysis locally, from inspecting CT/MRI/X-ray studies and running containerized segmentation models like MedSAM2, TotalSegmentator, and HD-BET to refining masks, measuring volumes, and writing reports. It pairs preset or custom guideline protocols with a code-customizable viewer that renders images back to agents as annotated PNGs and to humans as interactive HTML.

Enables AI agents to generate images, videos, and lip-sync media, and to query wallet balances via the Open Generative Studio instance.

Exposes Open Video Guide functions to MCP clients, enabling conversion of tutorial videos into illustrated, verifiable guides with timestamps, evidence, and review states.

A server that integrates Blender with local AI models via the Model Context Protocol, allowing users to control Blender using natural language prompts for 3D modeling tasks.

Enables intelligent document processing by extracting text, classifying document types, and generating structured summaries from PDFs and images using vision LLMs.

Enables recognition and extraction of architectural CAD drawings (DWG) by creating tasks and retrieving structured results for frames, title blocks, and 23 building component types, including rooms, doors, windows, stairs, and text.

This server enables interaction with Google's Video Intelligence API for advanced video analysis, auto-generated using AG2's MCP builder to provide a standardized multi-agent interface.

Enables AI agents to control a live Blender session, manage scene objects and materials, render images, fetch PolyHaven assets, and send prompts to configurable local or OpenAI-compatible LLM providers with runtime switching.

Enables AI agents to inspect and modify live Blender scenes, manage objects and materials, render images, retrieve PolyHaven assets, and send prompts to configurable local or OpenAI-compatible LLM backends.

Enables AI agents to control a live Blender session through the Model Context Protocol, including scene and object operations, rendering, PolyHaven asset integration, and prompt-driven workflows. It supports multiple local or OpenAI-compatible LLM backends that can be switched at runtime.

Enables AI agents to drive a live Blender session — inspecting scenes, creating, modifying and deleting objects, setting materials, rendering images, and searching or downloading PolyHaven assets and textures. It also routes prompts to a pluggable LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, Azure) that can be listed and switched at runtime without restarting.

Enables AI agents to control a live Blender session, creating, modifying, and deleting objects, applying materials, rendering images, executing code, and pulling PolyHaven assets, while routing prompts through a provider-agnostic LLM backend that can be switched at runtime.

Enables AI agents to control a live Blender session through the Model Context Protocol, including scene and object manipulation, rendering, PolyHaven asset access, and runtime switching between multiple LLM backends.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

Wraps Jimeng AI's image and video generation APIs for AI assistants to create and edit visual content via natural language.