"Uptime Kuma" matching MCP servers:

Independent latency and uptime data for 46 hosted AI inference APIs, probed every 5 minutes from 4 regions with no gateway, plus a verified model deprecation calendar and Claude Code / Codex speed and accuracy drift. Four read-only tools, no API key; data CC BY 4.0.

Multi-model vision understanding MCP server that provides unified image analysis for AI assistants without native vision, supporting models like GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, and more.

Enables AI coding assistants and agent workflows to query live measured free-LLM performance and availability, including throughput, uptime, tool-calling success, and model diagnostics.

Blocks prompt injection, jailbreaks, and out-of-scope input before an AI agent acts. classify_input tool, stdio transport.

An MCP server that adds visual understanding to text-only LLMs via image understanding, OCR, and image comparison tools, with multi-provider fallback and context-aware Focus Hint for precise descriptions.

A local RAG context optimizer MCP server that filters retrieved context to the minimum an LLM needs to answer correctly, using a cross-encoder and optional calibration.

Real-time status monitoring, uptime tracking, incident history, and API pricing for 42+ AI tools including ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, DeepSeek, and Groq. No API key required. Data updated every 5 minutes from independent monitoring infrastructure.

Find the most reliable AI agent for any task. Search 2,000+ agents across A2A and MCP with quality filters — min uptime, max latency, score thresholds. Check if an agent is alive before routing to it. Like Artificial Analysis, but for agent services.

Enables image and video generation using Luma's API, supporting multiple models, aspect ratios, and advanced features like keyframes and style references.

Adds image understanding to AI coding clients that lack native vision, letting them analyze screenshots, error messages, UI mockups and documents through a unified image_understand tool. Supports multiple vision LLM providers with automatic cropping, compression and retry handling.

Adds image understanding to AI assistants that lack native vision, letting them analyze code screenshots, error captures, UI layouts, documents, and OCR content through a single unified image_understand tool. It routes requests across multiple vision model providers such as GLM-4.6V, DeepSeek-OCR, Qwen3-VL, Doubao-Seed, and Hunyuan, handling large-image cropping, compression, retries, and HTTP/Docker deployment.

Provides a unified image-understanding tool for AI coding assistants without native vision, letting them analyze screenshots, error messages, UI layouts, and documents through multiple vision LLM providers. Supports local files, remote URLs, and Data URIs with preprocessing, OCR, multi-crop, retries, and HTTP/Docker deployment.

A multi-model vision MCP server that gives image understanding to AI coding assistants lacking native vision, letting them analyze screenshots, error messages, UIs, and documents through a single image_understand tool. It routes requests to major vision LLM providers such as GLM, DeepSeek-OCR, Qwen-VL, Doubao, and Hunyuan, with automatic image preprocessing, cropping, and retries.

Enables MCP clients to browse a live catalog of 100+ models with pricing, rankings and measured uptime, check credits and spend, and send chat completions through an OAuth-authenticated, per-connection key capped by a spend limit. Read tools are free, and paid calls are priced and approved before any credit is used.

Enables AI coding assistants without native vision to analyze images — screenshots, error messages, UI layouts, and documents — through a single image_understand tool. It routes requests across multiple vision LLM providers (GLM, DeepSeek-OCR, Qwen, Doubao, Hunyuan, or any OpenAI-compatible endpoint), with automatic cropping, compression, and retry handling for text-dense or large images.

Gives AI coding models without native vision the ability to analyze images — screenshots, error messages, UI mockups, and documents — through a single unified image understanding tool. It routes requests to multiple vision model providers (GLM, DeepSeek-OCR, Qwen, Doubao, Hunyuan, or any OpenAI-compatible endpoint) with automatic cropping, compression, OCR-friendly handling, and retries.

Enables AI coding clients that lack native vision to analyze screenshots of code, errors, UIs, and documents by routing images through a single image_understand tool backed by multiple vision LLM providers such as GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, Doubao-Seed, and Hunyuan-Vision. Handles local files, remote URLs, and Data URIs with automatic multi-crop, compression, retry, and OCR-friendly preprocessing, and can be deployed over stdio or shared HTTP/Docker for multiple clients.

Enables AI clients and coding models that lack native vision to analyze images — screenshots, error captures, UI mockups, documents and OCR — through a single unified image-understanding tool. Routes requests to multiple vision model providers (GLM, DeepSeek-OCR, Qwen, Doubao, Hunyuan or any OpenAI-compatible endpoint) with automatic cropping, compression and retry handling.

Enables AI assistants without native vision to analyze screenshots, code errors, UI, documents, and OCR images by routing requests to multiple vision model providers. It supports a unified image-understanding tool with local files, remote URLs, and Data URIs.

Enables AI coding models without native vision to analyze images such as screenshots, error messages, UI layouts, and documents through a single image_understand tool. Supports multiple vision LLM providers including GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, Doubao-Seed-1.6, Hunyuan-Vision, and any OpenAI-compatible endpoint.

Enables AI clients and coding models without native vision to analyze images — screenshots, code errors, UI layouts, documents and OCR — through a single image_understand tool. Routes requests to multiple vision model providers with automatic preprocessing, multi-crop, compression, retry, and optional HTTP/Docker deployment for shared access.

Adds image understanding to AI coding clients that lack native vision, letting them analyze screenshots, error messages, UI layouts, and documents through a single unified tool. Routes requests across multiple vision LLM providers with automatic preprocessing, multi-crop handling for large or text-dense images, and retry logic.

A multi-model vision MCP server that gives image understanding to AI coding models lacking native vision, letting them analyze screenshots of code, errors, UI, and documents or perform OCR. It routes requests through major vision LLM providers and can be shared over local networks via HTTP or Docker.

Enables AI coding models without native vision to analyze screenshots, errors, UI, documents, and OCR images through a single image_understand tool using multiple major vision LLM providers.

Enables AI coding assistants without native vision to analyze images such as screenshots, error messages, UI mockups, and documents through a single image_understand tool backed by multiple mainstream vision model providers. It handles local files, remote URLs, and Data URIs with automatic cropping, compression, and retries, and can be deployed locally via stdio or shared over HTTP/Docker.

Provides a unified image-understanding tool for AI assistants without native vision, letting them analyze screenshots, error messages, UI layouts, and documents via multiple vision LLM providers. It supports local files, remote URLs, Data URIs, OCR, multi-crop processing, and standard MCP clients.

Enables AI assistants and coding models without native vision to analyze images—screenshots, error messages, UIs, documents and OCR text—through a single unified image_understand tool. Supports multiple vision LLM providers (GLM-4.6V, DeepSeek-OCR, Qwen3-VL, Doubao, Hunyuan, or any OpenAI-compatible endpoint), with automatic image preprocessing, cropping and retries, over stdio or shared HTTP/Docker deployment.

Enables AI coding assistants without native vision to analyze screenshots, errors, UI designs, and documents through a unified image understanding tool backed by multiple vision model providers. Supports local files, remote URLs, Data URIs, OCR, multi-crop processing, and stdio or HTTP deployment for shared access.

Enables AI assistants and coding models without native vision to analyze screenshots, code errors, UI, documents, and OCR images through multiple vision LLM providers via a single image_understand tool.

Adds image understanding to AI coding assistants that lack native vision, letting them analyze screenshots of code, errors, UIs, and documents through a single image_understand tool. Supports multiple mainstream vision LLM providers, with automatic preprocessing such as multi-cropping, compression, and retries.