"Uptime Kuma" matching MCP servers:

MCP server for StillOnline uptime monitoring, enabling management of projects, checks, incidents, and public status pages through natural language.

Zero-setup safety toolkit for AI coding agents with 16 built-in tools for context gathering, safe file editing, validation, and session memory. Features rollback, circuit breaker, sandbox, timeout, and dangerous pattern blocking — no config, no API key, no database required.

Provides Windows system diagnostic capabilities to AI agents, allowing them to access event logs, crash information, system uptime, and perform stability analysis.

Provides system information through a single tool that returns platform details, Node version, architecture, current time, and uptime. Optionally includes hostname when specified in the request.

Enables searching for and checking the reachability of MCP servers through real handshakes and uptime verification.

Provides unified access to multiple homelab services including Nginx Proxy Manager, Pi-hole, Uptime Kuma, Portainer, and UPS NUT for monitoring, management, and automation through a single modular interface.

Uptime monitoring your AI agent can set up: create HTTP, SSL, ping, TCP and DNS monitors checked from multiple regions, run checks on demand, read and acknowledge incidents, manage cron heartbeats, maintenance windows and status page updates.

Enables agents to inspect app status, uptime, response times, incidents, and certificate/domain expiry; set up and manage monitors and status pages; and draft incident updates through authenticated MCP tools.

Enables AI coding assistants to look up KUKA system variables and KSS alarms, lint and diagnose KRL projects, perform industrial safety audits, and read live controller telemetry from KRC4/KRC5 or virtual controllers.

Enables MCP clients to query an organisation's merged-PR history as a FalkorDB graph, computing bus factor, module risk and successor candidates with the evidence path behind each answer. It also forks the graph to simulate a person's departure, hand a plan through analyst, planner and reviewer roles, and draft reviewable succession artifacts such as CODEOWNERS patches and issues.

Provides real-time data for package versions, download counts, and cloud service statuses across npm, PyPI, and various service providers. It enables users to perform technical lookups and monitor service uptime through natural language commands.

Enables AI assistants to interact with GitHub repositories via the REST API, supporting repository listing, detail retrieval, and issue management.

Enables AI agents to plan software collaboratively with humans by creating and editing graph structures over MCP, then exporting them as Markdown trees and Obsidian Canvas files.

Connects AI assistants directly to IBM Maximo environments to discover APIs, inspect schemas, query live data, generate UI code, and create, update, or act on records through OSLC REST and business workflows.

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.

Validates and rewrites AI-generated text to match brand voice, supporting REST API, MCP, CLI, and GitHub Actions.

Enables beginners to explore GitHub repositories and issues, assess issue suitability based on their skills and experience, and generate step-by-step contribution roadmaps through an MCP-compatible AI assistant.

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 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 collecting data from configurable sources such as Jira, SonarQube, Uptime Robot, npm audit, and HTTP endpoints, then exporting it to an Excel file with one sheet per source.

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 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.

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.

Enables Claude to call any HTTPS API using vault-stored secrets without exposing credentials to the model, while enforcing per-secret host restrictions and approval for destructive actions.

Enables local batch image preparation for the web via MCP, including compression, resizing, metadata stripping, thumbnails, and favicon generation.

An MCP server that exposes Postman API requests as tools, enabling natural language interaction with APIs via MCP-compatible clients.

A multi-model vision MCP server that gives image understanding to AI coding models lacking native vision, letting them analyze screenshots, error images, UIs, documents, and OCR content through a single unified tool. It supports several mainstream vision LLM providers plus any OpenAI-compatible endpoint, with automatic image cropping, compression, and retry handling.