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Pulsetic Mcp Server

The Pulsetic MCP Server connects Pulsetic monitoring with AI agents and MCP-compatible tools, enabling direct access to uptime data, cron monitoring results, incident management workflows, and status page information through the Model Context Protocol (MCP). It allows teams to securely expose operational monitoring data in a structured format, making it easy to build AI-driven automation, monitoring assistants, and intelligent operational workflows without custom middleware. Core Capabilities Uptime Monitoring Access real-time and historical uptime data through AI systems. Query monitor health, availability metrics, and performance status directly from AI agents or automation workflows. Cron Monitoring Track scheduled jobs and background tasks with cron monitoring insights. Detect missed executions, delays, or failures and integrate alerts into AI-powered operational processes. Incident Management Retrieve incident history, status updates, and timelines programmatically. Automate incident analysis, reporting, and response workflows using AI tools. Status Pages Integrate public or internal status page data into AI environments. Automatically surface service status, ongoing incidents, and maintenance updates for improved transparency and communication. Use Cases AI-powered uptime and reliability monitoring Automated incident response and reporting Intelligent cron job supervision Status page automation and operational visibility Internal AI assistants for DevOps and SRE teams The Pulsetic MCP Server brings uptime monitoring and operational intelligence directly into modern AI ecosystems, enabling smarter automation, faster troubleshooting, and improved service reliability.

Rival Mcp

rival-mcp MCP server for querying AI model comparison data from rival.tips This server lets AI coding assistants — Claude Code, Cursor, Windsurf, and any MCP-compatible client — natively query model benchmarks, pricing, capabilities, and side-by-side comparisons without leaving your editor. Quick Start npx rival-mcp No API key required. All data is served from the public rival.tips API. Configuration Claude Code Add to your .claude/settings.json (project-level) or ~/.claude/settings.json (global): { "mcpServers": { "rival": { "command": "npx", "args": ["-y", "rival-mcp"] } } } Claude Desktop Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows): { "mcpServers": { "rival": { "command": "npx", "args": ["-y", "rival-mcp"] } } } Cursor Add to your Cursor MCP settings (.cursor/mcp.json): { "mcpServers": { "rival": { "command": "npx", "args": ["-y", "rival-mcp"] } } } Windsurf Add to your Windsurf MCP config: { "mcpServers": { "rival": { "command": "npx", "args": ["-y", "rival-mcp"] } } } Available Tools list-models List all AI models with optional filtering. Parameters: Parameter Type Description provider string (optional) Filter by provider: OpenAI, Anthropic, Google, Meta, Mistral, etc. category string (optional) Filter by category: flagship, reasoning, coding, small, free, image-gen capability string (optional) Filter by capability: chat, code, vision, image-gen, function-calling q string (optional) Free-text search across name, ID, provider, and description Example prompts: "List all Anthropic models" "Show me free models" "What models support vision?" get-model Get detailed information about a specific model — benchmarks, pricing, capabilities, unique features, and provider availability. Parameters: Parameter Type Description id string (required) Model ID, e.g. gpt-4.1, claude-3.7-sonnet, gemini-2.5-pro Example prompts: "Get details on claude-3.7-sonnet" "What are the benchmarks for gpt-4.1?" compare-models Compare 2-3 models side by side — benchmarks, pricing, capabilities, and shared challenges. Parameters: Parameter Type Description models string (required) Comma-separated model IDs (2-3). Example: gpt-4.1,claude-3.7-sonnet Example prompts: "Compare GPT-4.1 vs Claude 3.7 Sonnet" "How does Gemini 2.5 Pro stack up against GPT-4.1 and Claude Sonnet?" search-models Search for models by name, description, or capability when you don't know the exact model ID. Parameters: Parameter Type Description query string (required) Search query, e.g. vision, cheap coding, fast reasoning Example prompts: "Find models good at coding" "Search for cheap reasoning models" Development # Install dependencies npm install # Run in development mode npm run dev # Build for production npm run build # Run the built server npm start How It Works This MCP server communicates over stdio (standard input/output) using the Model Context Protocol. When an AI assistant needs model comparison data, it calls the appropriate tool, which fetches data from the rival.tips public API and returns structured JSON. The server exposes no resources or prompts — only tools. All data is read-only and publicly available. Data Source All model data comes from rival.tips, an AI model comparison platform featuring: 60+ AI models with benchmarks, pricing, and capability data Side-by-side comparisons with shared challenge responses Community-driven AI duel voting and rankings Pre-generated showcase responses across coding, creative, and reasoning tasks License MIT

Screenmonitormcp

ScreenMonitorMCP - Revolutionary AI Vision Server Give AI real-time sight and screen interaction capabilities ScreenMonitorMCP is a revolutionary MCP (Model Context Protocol) server that provides Claude and other AI assistants with real-time screen monitoring, visual analysis, and intelligent interaction capabilities. This project enables AI to see, understand, and interact with your screen in ways never before possible. Why ScreenMonitorMCP? Transform your AI assistant from text-only to a visual powerhouse that can: Monitor your screen in real-time and detect important changes Click UI elements using natural language commands Extract text from any part of your screen Analyze screenshots and videos with AI Provide intelligent insights about screen activity Core Features Smart Monitoring System start_smart_monitoring() - Enable intelligent monitoring with configurable triggers get_monitoring_insights() - AI-powered analysis of screen activity get_recent_events() - History of detected screen changes stop_smart_monitoring() - Stop monitoring with preserved insights Natural Language UI Interaction smart_click() - Click elements using descriptions like "Save button" extract_text_from_screen() - OCR text extraction from screen regions get_active_application() - Get current application context Visual Analysis Tools capture_and_analyze() - Screenshot capture with AI analysis record_and_analyze() - Video recording with AI analysis query_vision_about_current_view() - Ask AI questions about current screen System Performance get_system_metrics() - Comprehensive system health dashboard get_cache_stats() - Cache performance statistics optimize_image() - Advanced image optimization simulate_input() - Keyboard and mouse simulation