"A list of all remote servers" matching MCP servers:

Enables natural language queries of the Chrome UX Report API to fetch Core Web Vitals (LCP, INP, CLS, FCP, TTFB), compare form factors, benchmark pages against site averages, and analyze up to 40 weeks of performance trends.

Enables agents to query GPU availability across SSH hosts, find free GPUs, list hosts, and inspect fleet status using only nvidia-smi on the remote side.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

Provides covenant rule enforcement, hash-chained audit logs, and integrity verification for MCP-compatible agents. It enables users to define granular permission rules and maintain a tamper-evident audit trail of all actions.

Enables centralized management and monitoring of multiple distributed worker MCP servers. Provides tools to list workers, get status of all workers, and query specific worker status.

Enables unified management of maintenance windows and incidents across Atlassian Statuspage and Uptime Kuma. It allows AI assistants to schedule maintenance, update service statuses, and list monitors through a single MCP-compatible interface.

Enables natural-language investigation of Datadog data including logs, metrics, monitors, traces, hosts, dashboards, events, and incidents, all through read-only API access.

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.

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 users to log AI tool activity without conversation text and contribute back only user-approved, small-cell-suppressed counts through a local MCP interface.

A live post-mortem debugger MCP server for Home Assistant that lets AI agents list captured exceptions, walk traceback frames, read local variables, and optionally evaluate Python in the context of a captured frame for true post-mortem debugging.

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.

Remote MCP deployment scaffold for Skylight's MCP server, hosted on Cloudflare Workers and Containers, enabling HTTP access to the stdio-based Ruby MCP server.

A thin MCP server wrapper that automatically audits all tool calls via Attestive's AuditClient, logging decisions and enabling verification of the hash chain. It provides an example server with audited tools and demonstrates end-to-end recording and verification through the MCP protocol.

Read-only MCP server for observing Linux hosts (systemd, ZFS, Docker, VMs, network, hardware, NixOS) as a graph of objects with provenance. Provides seven tools to list hosts, get status, collections, objects, evidence, lookup, and changes.

Enables MCP clients to observe a remote Hermes deployment's agents, sessions, crons, skills, and tools, and to run one-shot remote agent chats via a chat tool.

Enables AI assistants to list OpenObserve log streams, inspect their schemas, and run read-only SELECT queries with enforced safety caps.

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.

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.

Enables AI assistants to diagnose a Python backend stack — Postgres, Celery, Redis and logs — in a single tool call, returning findings, a merged timeline of events, and possible cause-effect chains. All checks are strictly read-only, so an assistant can inspect blocked queries, missing workers, queue backlogs, Redis memory pressure and log error spikes without ever modifying anything.