"An overview of Supabase" 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 assistants to monitor, analyze, and configure OWASP ModSecurity CRS via Docker, with tools for overview, drill-down, and rule/IP management.

Enables querying a self-hosted Plausible Analytics instance to retrieve compressed analytics data such as overview stats, top pages, traffic sources, and trends, with computed deltas and human-readable filters.

Connects AI assistants to the Vectra AI security platform to enable intelligent analysis of threat detection data and automated incident response workflows. It allows users to investigate threats, take response actions, and generate security reports using natural language.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Transforms Elasticsearch into an AI-powered observability engine for analyzing logs, APM traces, and system metrics through natural language.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

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.

Enables AI agents to analyze, cluster, compare, and export reports from local log files through seven read-only tools, returning compact evidence packs with line-number citations instead of raw log dumps. All processing happens on the local machine, so log data never leaves the environment.

Enables querying privacy-focused website analytics from Plausible's read-only Stats API v2, with tools for overview, timeseries, breakdown, and goal performance metrics.

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

Compresses log files into templates and statistics using Drain3, and exposes them to AI assistants via an MCP server for efficient log monitoring and anomaly detection.

Provides telemetry tools for retrieving recent logs and system metrics to support root-cause analysis of infrastructure incidents. Enables autonomous incident triage with grounded verification and human-in-the-loop remediation.

An MCP server for Auvik network management, enabling monitoring, management, and visibility of network devices and infrastructure through Auvik's API.

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

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

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