"How to use or perform actions in Apple Calendar" matching MCP servers:

Runtime access for AI coding agents to observe and control Android and iOS apps.

Local MCP server for Apple platform console, unified, and diagnostic logs. Enables listing devices and capturing live logs from simulators and real devices.

Enables tracing mcp-use tool callbacks and capturing tool errors and exceptions in Sentry via MCP middleware, without SDK changes or additional services.

Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Enables AI agents to monitor server health and capacity, diagnose outages, inspect Docker and deployment status, and perform safe, bounded recovery actions via MCP without granting unrestricted shell or SSH access.

Read-only MCP server that exposes Grafana dashboards, datasources, and metric/log queries as tools, for use by skills in the TICKETS workspace.

Lets you use Claude Desktop, or any MCP Client, to use natural language to accomplish things on your Cloudflare account.

Enterprise AI operator evaluation MCP server. 27 tools (22 read + 5 write) for measuring, benchmarking, diagnosing, and interv ening on how human operators use AI tools across 5 canonical metrics.

Enables agents to score, inspect, and gate tool calls before they execute, resolving shell commands the way a shell would in order to block destructive actions and detect multi-step attack sequences. It also redacts credentials and PII in either direction, requires signed single-use approval tokens for high-impact actions, and keeps a tamper-evident audit trail.

Enables AI agents to inspect a live Celery deployment over RabbitMQ or Redis — checking which workers are responding, their pools, concurrency and consumed queues, and how many messages are backed up in each queue — so agents can diagnose stuck or unrouted tasks. Read-only by default, with broker credentials masked in all output.

Enables MCP-compatible AI assistants to inspect and manage local or remote servers in plain language, covering system health, Docker containers, services, ports, disks, scoped files, and read-only database queries over key-only SSH. Write actions such as container restarts, file edits, and allowlisted dev commands require client approval.

Enables grading a website's security headers with per-gap fixes and a README badge, and analyzing logs, code, configs or suspicious messages to determine what happened, how severe it is, and what to fix first. Requests are masked for sensitive data and automatically retried across multiple AI providers when one refuses, subject to a defensive-use-only policy.

Enables AI agents to perform safe, auditable Kubernetes write actions such as scaling, restarting and rolling back Deployments through a plan → policy-check → approve → apply → audit flow, with server-side guards, single-use plans and scoped RBAC. Read-only planning and Prometheus SLO/alert tools support the same guarded workflow.

Enables secure enterprise AI agents to access internal tools like GitHub, Gmail, Calendar, file systems, databases, and knowledge bases through the Model Context Protocol, with built-in security, audit, and observability.

Enables Codex users to check MCP connectivity and inspect recent Computer Use call records, paired hook events, and missing or conflicting observations for reliable app and window targeting.

Analyzes how search engines and AI systems discover, crawl, cite, and convert traffic via CLI and MCP tools.

An observable autonomy harness for AI coding agents that enables long task execution without drift or cheating, while recording all actions for cost and quality steering.

Enables AI agents to perform one-shot triage of Playwright trace files, pinpointing root causes like backend API failures or locator issues and generating self-healing code fixes in a single low-token call.

Enables MCP clients to inspect and manage one or more Technitium DNS Server instances through their HTTP API, including stats, zones, records, query logs, and optional write actions.

Enables AI assistants to query servers, uptime monitors, incidents, DNS, domains, certificates, and audit logs in plain language, with limited on-call actions when permitted.

Enables AI agents and SREs to diagnose Kubernetes incidents, perform root cause analysis, and generate safety-guardrailed GitOps remediation patches or kubectl commands. It supports live clusters, Alertmanager webhooks, and offline simulation scenarios.

Provides read-only visibility into local CPU pools and Apple GPU lane usage inside Claude Code and Codex.

Enables incident detection and analysis by identifying anomalies in metric time series and surfacing root-cause candidates and recommended actions. Supports both mock (synthetic) and VictoriaMetrics backends with identical MCP tool contracts for seamless development-to-production switching.

SentinelOps is an MCP server that enables AI-powered security operations with human-in-the-loop safety. It allows agents to investigate threats, propose actions, and execute security responses only after explicit human approval.

Provides MCP tools to perform Looker health checks (health_pulse, health_analyze, health_vacuum) via the MCP Toolbox connector.

Enables agents to run bounded local profiling, benchmarking, and trace analysis on explicit artifacts or commands, returning typed evidence with provenance, coverage, limitations, and recovery actions. It also exposes optional evidence preservation and inspection through MCP tools.

Enables coding agents to access complete browser flow recordings—including user actions, network traffic, console logs, storage, DOM events, and screen recordings—so they can debug issues and perform regression testing without reproducing them manually. It also supports sharing captured flows via access tokens for team collaboration.