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590,918 tools. Updated 2026-09-20 09:30

"Understanding local computer vision models like YOLO" matching MCP tools:

  • Get local model recommendations for your specific task. Ranked by task match and hardware fit, showing which models run on your device's GPU or RAM.
    Creative Commons Attribution Non Commercial No Derivatives 4.0 International
  • List xAI API models, local Grok CLI models, and .grok model profiles separately to compare available options.
    MIT
  • List Oracle Cloud Infrastructure AI/ML services including Generative AI, Vision, Speech, Language, and Document Understanding. Filter by service type or model.
    Apache 2.0
  • Discover the capabilities, supported models, and limits of the vision-mcp server before using other analysis tools.
    MIT

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  • Image/video analysis: NSFW detection, object detection, thumbnails

  • Find local businesses on Google: name, address, phone, hours, ratings, and photos.

  • Discover which AI models are available on your NVIDIA account. Filter by capability code, vision, reasoning, or embedding to find the right model.
    MIT
  • List models available on your API key and identify which ones the server defaults to for image generation and vision tasks.
    MIT
  • Identify objects, text, or anomalies in a local image by specifying a task. Get structured JSON results from AI vision analysis.
    MIT
  • Check if your main model supports native vision to decide whether Atlas Vision tools are needed. Use before analyze_image or ocr_image for text-only models.
    MIT
  • Analyze local image files or URLs with Google's Gemini vision models to extract text descriptions or answer questions about screenshots, diagrams, and UI states without loading raw image bytes.
    MIT
  • Understand video content with AI vision: extract key moments, identify objects, people, and activities from local files or URLs.
    Apache 2.0
  • Inspect the vision backend status, covering models, backend type, ffmpeg, speech environment, GPU, and watchdog. Verify readiness and diagnose configuration issues.
    MIT
  • Send prompts to Google Gemini AI models through a secure CLI interface. Include local files using @path syntax and configure execution modes for safe interactions.
    MIT
  • Install a Blockbench plugin by ID, URL, or local path to enable plugin-specific formats like GeckoLib models.
    MIT
  • Discover top-performing AI models by category like coding, math, or vision. Filter by context window, release date, and limit results to find suitable models for specific tasks.
    MIT
  • Analyzes local images or screenshots with a vision-capable LLM, returning detailed textual descriptions so text-only models can interpret visual content.
    MIT
  • Retrieve SFRA documentation filtered by functional categories like core classes, product models, order/cart functionality, customer management, pricing, or store models to explore related documentation and understand functional groupings.
    MIT
  • List registered vision model providers with their default models and configuration status to identify which providers are ready to use and avoid errors from missing API keys.
    MIT