"Assistance with macOS Spaces feature" matching MCP servers:

Enables secure execution of Python code, SQL queries, and metric fetching through MCP with ephemeral Docker sandboxing.

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.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

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

Enables real-time data streaming through Server-Sent Events with timestamp broadcasting and server monitoring capabilities. Optimized for deployment on Render.com with health checks and status endpoints.

Provides tools for obtaining system CPU and memory statistics through FastAPI endpoints exposed via MCP.

Enables AI agents to create, run, and monitor scheduled HTTP jobs on the smplkit platform via natural language commands.

Connects AI assistants to PostHog to query events, persons, insights, dashboards, feature flags, cohorts, and experiments through the Model Context Protocol.

Enables AI agents to query PostHog analytics data directly via tool calls, including insights, events, feature flags, trends, and persons.

Enables querying and managing PostHog projects, insights, feature flags, and experiments through MCP tools backed by the PostHog REST API.

A comprehensive MCP toolkit for Java backend developers, providing 35 tools across 5 servers for database analysis, JVM diagnostics, migration assistance, Spring Boot monitoring, and Redis diagnostics.

Enables interaction with PostHog's product analytics platform through natural language, allowing users to manage feature flags, view errors, and access other platform features.

Exposes a live application's runtime state to coding agents, including logs, DB schema, config, and feature flags, with read-only SQL and secret redaction.

Enables AI assistants to interact with Statsig's feature management and experimentation platform, allowing them to check feature flags, retrieve configurations, get experiment assignments, and log events.

Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.

Provides ML-powered revenue intelligence for sales and customer success teams, enabling lead scoring, churn risk detection, and conversion predictions with explainable feature attribution and production monitoring capabilities.

An MCP server for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

Enables querying logs and metrics with plain SQL from any MCP client via pooml's read-only query API.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

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

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

Enables AI agents to discover and call Xyte's device management API endpoints with typed validation, supporting fleet investigation, ticket management, and routine operations through natural language.

A read-only kubectl MCP server for AI assistants. The kubectl verb is hardcoded in each tool (get/describe/logs/events) and user input only fills argument values — no mutation path even with malicious input, and Secret/ConfigMap values are never returned (metadata only). Returns structured JSON, plus one-shot k8s_triage (health scan) and k8s_inventory (cluster snapshot).

Provides Claude with read-only access to PC diagnostics including hardware specs, live performance metrics, disk usage, network checks, and more, enabling natural language queries about PC health and performance.

Enables agents to fetch app reviews from Apple's App Store and Google Play per country store, newest first, filtered by date or star rating, with each scheduled run returning only reviews not seen before. It also returns a per-app, per-country rating trend row with the current rating, rating count, change since the last run, and the star split of new reviews.

A Model Context Protocol server that provides LLM Agents with a comprehensive toolset for IP geolocation, network diagnostics, system monitoring, cryptographic operations, and QR code generation.

Enables AI agents to self-report bugs, ambiguities, and improvement requests via a single report_issue tool, logging to PostgreSQL or JSONL with severity and mode classification.

MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.

Enables LLM agents to monitor Mac thermals and control fan speed with safety limits enforced by a root daemon.

🔌 Complete MCP server for Zabbix integration - Connect AI assistants to Zabbix monitoring with 40+ tools for hosts, items, triggers, templates, problems, and more. Features read-only mode and comprehensive API coverage.

Minimal MCP server that integrates with Graylog, enabling agents to search, analyze, and inspect log messages across streams, discover fields and streams, and query multiple Graylog instances.

Demonstrates agent-native platform onboarding with human-in-the-loop API key provisioning, plus live usage and rate-limit queries via Anthropic Admin APIs.

Read-only MCP server for CometBFT/Cosmos validator operators: checks signing liveness and downtime-jail risk, diagnoses chain halts (who isn't voting), and reports peer topology and upgrade readiness. Keyless and GET-only, with node IPs masked by default.

Independent latency and uptime data for 46 hosted AI inference APIs, probed every 5 minutes from 4 regions with no gateway, plus a verified model deprecation calendar and Claude Code / Codex speed and accuracy drift. Four read-only tools, no API key; data CC BY 4.0.

Uptime monitoring for websites, APIs, SSL certificates, domain expiry, ping and TCP/UDP ports. 15 tools to list, create, pause and delete monitors, pull incident timelines with error codes, and read hourly or daily uptime and response-time statistics.

A Model Context Protocol (MCP) server for interacting with Joe Sandbox Cloud. This server exposes rich analysis and IOC extraction capabilities from Joe Sandbox and integrates cleanly into any MCP-compatible application (e.g. Claude Desktop, Glama, or custom LLM agents).

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.

Yandex Metrica over MCP: 13 tools for counters, goals, Reporting API queries with period comparison, and the full Logs API export lifecycle with preview-first downloads.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

DBeast MCP is a robust, security-first Model Context Protocol server designed to empower AI coding assistants (like Claude Desktop) with deep PostgreSQL database insights. Instead of just running basic SQL queries, DBeast equips your AI agent with tools to inspect active locks, audit slow queries, analyze table schemas, and safely generate index recommendations in real-time. Built specifically for