"iOS" matching MCP servers:

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

An MCP server for streaming and searching macOS Console.app and iOS device logs, enabling real-time log access and debugging through natural language.

Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.

MCP server for Drumbeats monitoring. Enables creating monitors, triaging incidents, and running HTTP/SSL/DNS checks using natural language from any AI client.

Provides transactional intelligence for AI agents, enabling safe tool execution with pre-flight invariant checks, sub-second filesystem snapshots/rollback, causal tracing, belief contradiction detection, and 15 native MCP tools for Claude Code.

iOS leak hunting and performance investigation. Reads .memgraph and .trace files, classifies retain cycles against a 34-pattern catalog with Swift fix templates, bridges to source via SourceKit-LSP. 28 MCP tools, 34 catalog resources, 5 investigation prompts.

eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime and Driver APIs via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. 7 tools: get_check, get_trace_stats, get_causal_chains, get_stacks, run_demo, get_test_report, run_sql. Telegraphic compression reduces token usage ~60%. Supports stdio and HTTPS (TLS 1.3) transport.

Enables AI agents to build, test, debug, and interact with Kotlin Multiplatform Mobile (Android/iOS) applications through automated build pipelines, UI automation, crash analysis, and app state inspection.

Read-only-by-default MCP server for Datanika, an open-source dlt + dbt-core data pipeline platform: browse connections, introspect and preview source tables, compile and run dbt transformations, and monitor pipeline runs. 25 tools (17 read-only, plus 8 write tools gated behind --allow-write); source lives in the datanika-mcp/ subdirectory of the repo.

Enables network engineers and AI agents to answer troubleshooting questions like "why can't A reach B?" by choosing typed tools and filling parameters, with the server rendering deterministic, pre-shaped queries across firewall, VNet flow, Cisco IOS and Cisco ISE logs in Azure Log Analytics. The agent never writes raw query language, keeping the blast radius bounded to a fixed, closed tool catalog that runs locally on synthetic data or against a real workspace.

Enables MCP clients to interact with a local AI agent through Ollama, providing tools for checking weather, current time, and available models, while tracing calls to Langfuse.

A portable, read-only Model Context Protocol server for turning observability data into bounded evidence that AI agents can inspect safely.

Query and interact with kubernetes environments monitored by Metoro. Look at APM, metrics, traces, profiling information with LLMs.

MCP server that answers service status and uptime questions by reading Atlassian Statuspage feeds from hundreds of vendors, with keyless access and tools for checking status, incidents, and vendor listings.

Enables ChatGPT users to view and inspect nodes, jobs, executions, recent errors, and bounded job logs in linked workspaces through read-only MCP tools, including sidebar views and mention-based queries.

Enables AI clients to connect to live IoT deployments, read devices, provision hardware, deploy automation rules, manage alarms, and send commands to physical equipment.

Provides AI assistants with direct access to Red Hat OpenShift AI observability data, enabling querying of Prometheus metrics, Alertmanager alerts, Loki logs, Grafana dashboards, and Kubernetes cluster state to troubleshoot vLLM inference workloads.

Enables MCP clients to ask permission before an agent acts, returning allow, deny, or ask_human with receipts, and to monitor agent spend, limits, and recent decisions.

Enables querying real-user Core Web Vitals field measurements — 75th-percentile LCP, INP, and CLS with distributions and ~40 weeks of history — for any origin or page that has enough Chrome traffic.

Enables querying Cloudflare Radar data for internet traffic, attacks, BGP, and quality insights via natural language or direct tool calls.