"Using iCalendar on macOS" matching MCP servers:

Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.

Enables AI agents to profile iOS/macOS apps using Xcode Instruments, automate simulator interactions, and get structured performance data for optimization.

MCP server for On-Call Health burnout analysis. Connects AI assistants to your on-call data for workload insights.

Provides read-only on-demand ServiceNow reports for system logs (syslog) and developer work (update set changes), with automatic pagination and date filtering.

Provides advanced system monitoring and file search capabilities for macOS, allowing users to track performance metrics and perform enhanced file searches with content analysis and tagging features.

Provides structured MCP tools for local runtime observability and debugging of React Native/Expo apps on Android, enabling evidence-first diagnostics such as screenshots, UI trees, per-request network capture, performance metrics, and CDP-based console/heap/profiling exports.

An MCP server that exports, symbolicates, analyzes, and reports iOS/macOS crash logs using natural language, with integration for Apptics, Zoho Cliq, and Zoho Projects.

Enables MCP clients to inspect Google Play releases, reviews, Android vitals, and scoped bulk reports, and to prepare explicitly authorized changes using locally supplied service-account credentials. Reads are default, while optional writes and sensitive reads require explicit configuration and single-use execution.

MCP server for system monitoring, enabling users to ask about CPU, memory, disk, network, and processes on their system.

Bridges Microsoft Clarity's analytics API with Claude, enabling custom date ranges and page-level filtering on top of Clarity's native limitations.

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Enables intelligent monitoring and management of Zoom rooms across multiple sites using smart location resolution and hierarchical discovery. It provides tools for checking room status, retrieving location hierarchies, and accessing detailed room configurations through the Zoom API.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

It enables solo builders to automatically log product changes to PostHog and later get statistically honest verdicts on whether those changes moved key metrics, without needing enough traffic for A/B tests.

Lets MCP clients answer questions about website traffic using Plausible Analytics Stats API v2 through read-only tools for totals, trends, breakdowns, and realtime visitors.

Tamper-evident audit logging for AI decisions. Three tools (record_decision, verify_decision, list_decisions) write to a regulator-grade ledger built on AWS S3 Object Lock with 7-year retention. Designed for EU AI Act Article 12 and FCA SS1/23 evidence requirements. Try zero-config: npx audit-ledger-mcp boots in sandbox mode against a public hosted tenant.

Read-only MCP server exposing AGmind Systems Lab's measured local-LLM benchmark claims (TTFT, inter-token latency, task success, etc.) on AMD Strix Halo and DGX Spark hardware via search, claim lookup, and measured-combination listing tools.

An MCP server that exposes a dbt project's run state as tools, enabling AI assistants to answer questions like 'is the warehouse healthy?' or 'what broke and why?' via plain English, using read-only access to dbt artifacts and warehouse data.